How is Remote Sensing used?
Zippia reviewed thousands of resumes to understand how remote sensing is used in different jobs. Explore the list of common job responsibilities related to remote sensing below:
- Collected information about specific features of the Earth using aerial photography and other digital remote sensing techniques.
- Summer internship working within the Geographic Studies Branch of the Population Division towards the DEMOBASE product using remote sensing techniques.
- Served as project manager/technical lead for remote sensing/GIS study projects.
- Perform analysis and data integration principles and techniques through the employment of geospatial information systems and remote sensing extraction processes.
- Acquired practical knowledge of Geographic Information Systems and Remote Sensing applications in environmental assessment.
- Used remote sensing to classify water irrigation patterns for a resource conservation analysis
Are Remote Sensing skills in demand?
Yes, remote sensing skills are in demand today. Currently, 1,630 job openings list remote sensing skills as a requirement. The job descriptions that most frequently include remote sensing skills are geographer, meteorologist, and cartographic technician.
How hard is it to learn Remote Sensing?
Based on the average complexity level of the jobs that use remote sensing the most: geographer, meteorologist, and cartographic technician. The complexity level of these jobs is advanced.
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What jobs can you get with Remote Sensing skills?
You can get a job as a geographer, meteorologist, and cartographic technician with remote sensing skills. After analyzing resumes and job postings, we identified these as the most common job titles for candidates with remote sensing skills.
Geographer
Job description:
A geographer studies nature, land, and the earth's features. They gather geographic data through aerial field observations, maps, and satellite images. They conduct research through surveys, interviews, and other qualitative methods. They present the results of their research findings by writing reports. They may also advise others in using GIS, remote sensing, and GPS.
- Spatial Data
- ArcGIS
- Remote Sensing
- Python
- Data Collection
- Esri
Meteorologist
Job description:
A meteorologist specializes in understanding and interpreting atmospheric data, usually from weather stations and satellites, and delivering weather forecasts to the public through radio or television. Their responsibilities revolve around liaising with different external agencies, developing models for weather prediction, monitoring sea and land patterns, performing research and analysis, and keeping abreast of the latest developments. Furthermore, as a meteorologist, it is essential to update and monitor all records, all while maintaining an active line of communication with the team.
- Doppler
- Remote Sensing
- Satellite Imagery
- FAA
- Radar Data
- Atmospheric Administration
Cartographer
Job description:
A cartographer is someone who makes charts and maps based on geodetic surveys and satellite images. This person usually works with the government or a construction company to create urban planning and infrastructure development plans. The cartographer is an essential part of the construction business as it is necessary to know the lay of the land to build structures and edifices properly. The cartographer works alongside the architect and the overall engineer in a construction project.
- GIS
- Data Collection
- Extraction
- Remote Sensing
- GPS
- Bathymetric
Geospatial Technician
Job description:
Geospatial technicians majorly assist other geospatial analysts and project managers to build, manage, and make use of GIS databases to identify spatial relationships. Their job is to create customized maps and GIS applications or software. Furthermore, they are expected to review and interpret all GIS data, maps, and graphs. They are also expected to analyze applications of software, create data reports, and digital 3D models. Their job duties also involve updating satellite navigation systems and providing technical support to users.
- Python
- Visualization
- Production Tasks
- Remote Sensing
- Esri
- Extraction
Electro Optical Engineer
Job description:
An electro-optical engineer performs engineering tasks related to electronic and optical devices, creates optical designs, and assists users of laser optics. The essential skills that an electro-optical engineer should possess to perform his/her or her responsibilities include good mathematical and speaking skills and the ability to concentrate to create complex electrical systems and electronic components and products. The education requirements for the job include a college degree in electrical engineering, physics, or a related field.
- System Performance
- C++
- Data Analysis
- Remote Sensing
- RF
- LiDAR
Geospatial Analyst
Job description:
A geospatial analyst specializes in analyzing aerial imagery to develop geographic data that provides essential information about a ground or land's condition. They can find employment in different fields and industries such as agriculture, urban planning, mining, and even military intelligence. Although the extent of their duties varies upon their organization of employment, they typically involve creating maps and reports that highlight essential information, identifying geographical elements and structures, and providing recommendations to solve different issues and concerns.
- Geospatial Data
- Geospatial Analysis
- Remote Sensing
- Visualization
- Python
- Extraction
Remote Sensing Analyst
Job description:
A remote sensing analyst is an individual who analyzes data measured from aircraft, satellites, or ground-based platforms to infer what it means about the world. Remote sensing analysts use tools such as analysis software, image analysis software, or a geographic information system to display the results of findings. They are involved in some fieldwork to confirm their findings by taking field measurements. Remote sensing analysts must also monitor the quality of information that is gathered and should develop databases.
- Troubleshoot
- Epic
- Analyze Data
- Data Collection
- Remote Sensing
- LiDAR
How much can you earn with Remote Sensing skills?
You can earn up to $65,339 a year with remote sensing skills if you become a geographer, the highest-paying job that requires remote sensing skills. Meteorologists can earn the second-highest salary among jobs that use Python, $68,815 a year.
| Job title | Average salary | Hourly rate |
|---|---|---|
| Geographer | $65,339 | $31 |
| Meteorologist | $68,815 | $33 |
| Cartographic Technician | $51,358 | $25 |
| Cartographer | $62,172 | $30 |
| Geospatial Technician | $42,643 | $21 |
Companies using Remote Sensing in 2025
The top companies that look for employees with remote sensing skills are Pacific Northwest National Laboratory, Grant Thornton, and KBR. In the millions of job postings we reviewed, these companies mention remote sensing skills most frequently.
| Rank | Company | % of all skills | Job openings |
|---|---|---|---|
| 1 | Pacific Northwest National Laboratory | 14% | 323 |
| 2 | Grant Thornton | 12% | 1,087 |
| 3 | KBR | 9% | 1,455 |
| 4 | Department of Agriculture and Fisheries | 7% | 31 |
| 5 | Battelle | 6% | 246 |
Departments using Remote Sensing
| Department | Average salary |
|---|---|
| Business Development | $101,250 |
| Non Profit/Government | $90,806 |
19 courses for Remote Sensing skills
1. Remote Sensing using ArcGIS Pro
This is a coourse of applications of Remote Sensing using ESRI Products. Includes:#AulaGEO1) Introductioni. Course overview2) Introduction to ArcGIS Onlinei. Basics of ArcGIS Onlineii. ArcGIS Online Sign in and map vieweriii. ArcGIS Online map layout and toolsiv. Preparing a map in ArcGIS Onlinev. Introduction to ArcGIS Living Atlas3) Introduction to ESRI Story Mapi. Basics of ArcGIS Story Mapii. Overview of an example story mapiii. StoryMap layout (Part-A)iv. Storymap layout (Part-B)v. Final example of Story map4) Land use sciencei. Basics of Land use land cover (LULC) analysisii. Downloading satellite data from USGSiii. Importing data and applying preprocessing inside ArcGIS Pro (Part-A)iv. Importing data and applying preprocessing inside ArcGIS Pro (Part-B)v. Performing land use classification (Part-A)vi. Performing land use classification (Part-B)vii. Visualizing and preparing final maps for LULC in ArcGIS Pro5) Time series analysis for Urban Sprawl Analysisi. Background of Time Series, Urban Sprawl and Change Detectionii. Preparing LULC maps for time series analysisiii. Estimating area for each LULC class for each yeariv. Change detection in ArcGIS Prov. Publishing findings for study area using ESRI Story maps6) Urban Heat Island (UHI) Effecti. Basic concepts of UHIii. Evaluating LST from Landsat satellite in ArcGIS Pro (Part-A)iii. Evaluating LST from Landsat satellite in ArcGIS Pro (Part-B)iv. Evaluating UHI trends from LSTv. Evaluating UHI (Normalized) and UTFVI from LST...
2. Remote Sensing Image Acquisition, Analysis and Applications
Welcome to Remote Sensing Image Acquisition, Analysis and Applications, in which we explore the nature of imaging the earth's surface from space or from airborne vehicles. This course covers the fundamental nature of remote sensing and the platforms and sensor types used. It also provides an in-depth treatment of the computational algorithms employed in image understanding, ranging from the earliest historically important techniques to more recent approaches based on deep learning. It assumes no prior knowledge of remote sensing but develops the material to a depth comparable to a senior undergraduate course in remote sensing and image analysis. That requires the use of the mathematics of vector and matrix algebra, and statistics. It is recognised that not all participants will have that background so summaries and hand worked examples are included to illustrate all important material. The course material is extensively illustrated by examples and commentary on the how the technology is applied in practice. It will prepare participants to use the material in their own disciplines and to undertake more detailed study in remote sensing and related topics...
3. Fundamentals of Remote Sensing and Geospatial Analysis
Get this course for only 9.99. Use code JAN22AMASTER THE FUNDAMENTAL PRINCIPLES OF REMOTE SENSING AND GEOSPATIAL ANALYSIS! Do you find that other remote sensing courses are too short and vague, and do not prepare you for real world problems? Are you looking for a course that goes IN-DEPTH and teaches you all the fundamentals of remote sensing? My course provides a solid foundation to carry out practical, real life remote sensing spatial data analysis and gives you the techniques and knowledge to tackle a variety of geological and environmental problems. This course provides an introduction to remote sensing - the acquisition of information about the earth from a distance, typically via airborne and spaceborne sensors. The abundance of earth observation data allows us to address many pressing environmental, geographical, and geological issues. This course will prepare the students for the basics of using remote sensing data. Students will have a solid understanding of the physical principles of remote sensing, including electromagnetic (EM) radiation concepts, and will also explore in detail the interaction of EM radiation with the atmosphere, water, vegetation, minerals, and other land types from a remote sensing perspective. We will go over various industries where remote sensing can be used including agriculture, geology, mining, hydrology, forestry, environmental, and many more! The students will also learn about current satellite sensor platforms for remote sensing analysis including passive senors, synthetic aperture radar, and LiDAR. In addition to learning the basic concepts, terminology, and theories in remote sensing science and application, they will also learn the accurate steps for pre-processing images including radiometric and atmospheric correction techniques. Lastly, we will look at how to interpret spectral characteristics from different materials and how to create their own basic equation for using remote sensing images on their own projects in the future. Enroll in my course today! I want your experience with this course to be a success but more importantly prepare and give you the knowledge for using remote sensing in the real world...
4. Geospatial Analyses & Remote Sensing: from Beginner to Pro
Geospatial Data Analyses & Remote Sensing: 5 Classes in 1Do you need to design a GIS map or satellite-imagery based map for your Remote Sensing or GIS project but you don't know how to do this?Have you heard about Remote Sensing object-based image analysis and machine learning or maybe QGIS or Google Earth Engine but did not know where to start with such analyses?Do you find Remote Sensing and GIS manuals too not practical and looking for a course that takes you by hand, teach you all the concepts, and get you started on a real-life GIS mapping project?I'm very excited that you found my Practical Geospatial Masterclass on Geospatial Data Analyses & Remote Sensing. This course provides and information that is usually delivered in 4 separate Geospatial Data Analyses & Remote Sensing courses, and thus you with learning all the necessary information to start and advance with Geospatial analysis and includes more than 9 hours of video content, plenty of practical analysis, and downloadable materials. After taking this course, you will be able to implement PRACTICAL, real-life spatial geospatial analysis and tasks, including land use and land cover mapping and change detection, machine learning for GIS, data, and maps creation, etc. in popular and FREE software tools. This course is designed to equip you with the theoretical and practical knowledge of applied geospatial analysis, namely Remote Sensing and some Geographic Information Systems (GIS). By the end of the course, you will feel confident and completely understand the basics of Remote Sensing and GIS, learn Machine Learning applications in GIS / Remote Sensing technology, and how to use Machine Learning algorithms for various geospatial & Remote Sensing tasks, such as land use and land cover mapping (classifications) and object-based image analysis (segmentation, crop type mapping, etc). This course will also prepare you for using geospatial and Remote Sensing analysis with open source and free software tools. In the course, you will be able to apply in QGIS such Machine Learning algorithms like Random Forest, Support Vector Machines and Decision Trees (and others) for classification of satellite imagery. You will also learn how to download and process satellite imagery, conduct supervised and unsupervised learning, implement accuracy assessment, apply object-based image analysis, and change detection. On top of that, you will practice geospatial & Remote Sensing analysis by completing an entire classification project by exploring the power of Machine Learning, cloud computing, and Big Data analysis using Google Erath Engine for any geographic area in the world. In this course, I will teach you how to work with the popular open-source GIS & Remote Sensing, i. e. QGIS software, and its great tools: Semi-Automated classification plugin and Orfeo (OTB) toolbox. You will also get introduced to cloud computing and Big Data analysis using Google Erath Engine for any geographic area in the world. The course is ideal for professionals such as geographers, programmers, social scientists, geologists, GIS & Remote Sensing experts, and all other experts who need to use maps in their field and would like to learn more about Machine Learning in GIS & Remote Sensing and QGIS. If you're planning to undertake a task that requires to use a state of the art Machine Learning algorithms for creating, for instance, land cover and land use maps in QGIS and Google Earth Engine, this course will give you the confidence you need to understand and solve such geospatial problem. One important part of the course is the practical exercises. You will be given some precise instructions on Remote Sensing analysis, downloadable practical materials, scripts, and datasets to create maps and conduct analysis based on Machine Learning algorithms using the QGIS software and Google Earth Engine...
5. Fundamentals of Remote Sensing with Google Earth Engine
Do you want to learn the fundamentals of remote sensing?Do you want to learn how to access, process, and analyze remote sensing and GIS data using free open-source tools?Do you want to solve a real-world problem using freely available satellite data?Do you want to acquire new hands-on Remote Sensing skills that will prepare you for a remote sensing and GIS job in the geospatial industry?Enroll in this Fundamentals of Remote Sensing with Google Earth Engine course. I will provide you with hands-on training with examples of GIS and Remote Sensing data, sample scripts, and real-world applications. By taking this course, you will take your satellite remote sensing skills to the next level by gaining proficiency in satellite remote sensing and geospatial analysis with GEE, a cloud-based Earth observation, and GIS data visualization analysis powered by Google. In this Fundamentals of Remote Sensing and Image Analysis course, I will help you get up and running on the Google Earth Engine JavaScript API platform form to process and analyze geospatial data. By the end of this course, you will be equipped with a set of new Remote Sensing skills including accessing, downloading processing, analyzing, and visualizing GIS and Earth Observation big data. The course covers various topics including introduction to remote sensing, types of resolutions, remote sensing data sources, digital image processing, and image classification. In this course, I will use real satellite data including Landsat, MODIS, Sentinel-2, and others to provide you with a hands-on practical experience of working with freely available remotely sensed data. I will walk you through using a step by step video tutorials to process and analyze remote sensing data using a cloud platform. In addition to learning the basic concepts, theories, and terminologies, you will also learn the steps for image processing including mosaicking, resampling, reprojections, compositing, spectral unmixing, spectral transformation, and classification using real-world satellite data and example scripts. All sample data and scripts will be provided to you as an added bonus throughout the course. Jump in right now to enroll. To get started click the enroll button...
6. Complete Google Earth Engine for Remote Sensing & GIS
ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT BASIC SATELLITE REMOTE SENSING AND GIS ANALYSIS USING GOOGLE EARTH ENGINE (GEE). Are you currently enrolled in any of my GIS and remote sensing related courses?Or perhaps you have prior experience in GIS or tools like R and QGIS?You don't want to spend 100s and 1000s of dollars on buying commercial software for imagery analysis?The next step for you is to gain proficiency in satellite remote sensing data analysis and GIS using GEE, a cloud based endeavor by Google that can help process several petra-byte of imagery dataMY COURSE IS A HANDS ON TRAINING WITH REAL REMOTE SENSING AND GIS DATA ANALYSIS WITH GOOGLE EARTH ENGINE- A planetary-scale platform for Earth science data & analysis; powered by Google's cloud infrastructure. ! My course provides a foundation to carry out PRACTICAL, real-life remote sensing and GIS analysis tasks in this powerful cloud-supported paltform . By taking this course, you are taking an important step forward in your GIS journey to become an expert in geospatial analysis. Why Should You Take My Course?I am an Oxford University MPhil (Geography and Environment) graduate. I also completed a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life spatial remote sensing data from different sources and producing publications for international peer reviewed journals. In this course, actual satellite remote sensing data such as Landsat from USGS and radar data from JAXA will be used to give a practical hands-on experience of working with remote sensing and understanding what kind of questions remote sensing can help us answer. This course will ensure you learn & put remote sensing data analysis into practice today and increase your proficiency in geospatial analysis. Remote sensing software tools are very expensive, and their cost can run into thousands of dollars. Instead of shelling out so much money or procuring pirated copies (which puts you at arisk of prosecution), you will learn to carry out some of the most important and common remote sensing analysis tasks using one of the most powerful earth observation data and analysis platform. GEE is rapidly demonstrating its importance in the geo-spatial sector and improving your skills in GEE will give you an edge over other job applicants.. This is a fairly comprehensive course, i. e. we will focus on learning the most important and widely encountered remote sensing data processing and and GIS analysis techniques in Google Earth EngineYou will also learn about the different sources of remote sensing data there are and how to obtain these FREE OF CHARGE and process them using within GEE. In addition to all the above, you'll have MY CONTINUOUS SUPPORT to make sure you get the most value out of your investment! ENROLL NOW:)...
7. Satellite Remote Sensing Data Bootcamp With Opensource Tools
ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT BASIC SATELLITE REMOTE SENSING. Are you currently enrolled in either of my Core or Intermediate Spatial Data Analysis Courses? Or perhaps you have prior experience in GIS or tools like R and QGIS?You don't want to spend 100s and 1000s of dollars on buying commercial software for imagery analysis? The next step for you is to gain profIciency in satellite remote sensing data analysis. MY COURSE IS A HANDS ON TRAINING WITH REAL REMOTE SENSING DATA WITH OPEN SOURCE TOOLS! My course provides a foundation to carry out PRACTICAL, real-life remote sensing analysis tasks in popular and FREE software frameworks with REAL spatial data. By taking this course, you are taking an important step forward in your GIS journey to become an expert in geospatial analysis. Why Should You Take My Course? I am an Oxford University MPhil (Geography and Environment) graduate. I also completed a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life spatial remote sensing data from different sources and producing publications for international peer reviewed journals. In this course, actual satellite remote sensing data such as Landsat from USGS and radar data from JAXA will be used to give a practical hands-on experience of working with remote sensing and understanding what kind of questions remote sensing can help us answer. This course will ensure you learn & put remote sensing data analysis into practice today and increase your proficiency in geospatial analysis. Remote sensing software tools are very expensive and their cost can run into thousands of dollars. Instead of shelling out so much money or procuring pirated copies (which puts you at a risk of prosecution), you will learn to carry out some of the most important and common remote sensing analysis tasks using a number of popular, open source GIS tools such as R, QGIS, GRASS and ESA-SNAP. All of which are in great demand in the geospatial sector and improving your skills in these is a plus for you. This is an introductory course, i. e. we will focus on learning the most important and widely encountered remote sensing data processing and analyzing tasks in R, QGIS, GRASS and ESA-SNAP You will also learn about the different sources of remote sensing data there are and how to obtain these FREE OF CHARGE and process them using FREE SOFTWARE. In addition to all the above, you'll have MY CONTINUOUS SUPPORT to make sure you get the most value out of your investment! ENROLL NOW:)...
8. Complete Remote Sensing Image Analysis with ENVI Software
Get this course for only 9.99! Use code JAN22AAre you currently enrolled in my Fundamentals of Remote Sensing and Geospatial Analysis course and want to take your remote sensing knowledge to the next level? Are you already familiar with the field of remote sensing and want to learn how to process images?The next step for you is to gain proficiency in remote sensing data analysis using ENVI software! Go from zero to hero in remote sensing satellite image processing! My course provides a complete foundation to carry out practical and real life remote sensing image analysis processes using ENVI software. ENVI is the most widely used remote sensing and image analysis program within Industry and Research. In this course you will be using actual images and data from Landsat 8 and other popular satellites to give you hands on experience in image processing techniques. First we will go over the basic tools in ENVI and learn how to navigate the software. Then we will dive into and learn step by step the fundamental techniques in satellite remote sensing image processing such as: image mosaicingradiometric calibrationmultiple atmospheric correction techniques (Fast Line of Sight Atmospheric Analysis of Hypercubes and Dark Object Subtraction)supervised and unsupervised classificationvegetation indiesband ratiosand many more! Additional satellite images and data will be provided so that you can practice these techniques on your own. I will also provide you with additional resources that you can download and use in your future remote sensing career! We will also go over on how to locate and download FREE remote sensing satellite images! Once you have learned the basics of ENVI we will go into intermediate and advanced ENVI remote sensing processes such as: hyperspectral data analysis image registrationanomaly detectioncreating a burn index mapmineral mapping from hyperspectral imagesspectral angle mappertime series analysis pansharpening and much more! I hope that you ENROLL NOW and learn the remote sensing software that industry and research positions require! Start your remote sensing career here and learn the basics of remote sensing image analysis using ENVI software!...
9. Geospatial Data Analysis:Introductory GIS and Remote Sensing
This course is for those new to mapping and GIS, as well as anyone looking to gain a better understanding of how it all works and why. You will learn practical skills that can be applied to your own work using cutting-edge software. We have created sections to cover GIS basics, and we dedicated a section to LiDAR and UAV/Drone data processing, both techniques are booming nowadays (even the self driving cars companies are using LiDAR technology!). The main topics we cover are: GIS Basics- Data types- Software Introduction- Raster Data- Vector Data- Free Data Sources- Web Map Services Applications- Surveying - Hydrology- Telecom - LiDAR Data- UAV Data (Photogrammetry)- Heat MapsTo get started click the enroll button. I hope to see you in the course soon! TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success. Best...
10. Crop Yield Estimation using Remote Sensing and GIS ArcGIS
Crop yield estimation is a critical aspect of modern agriculture. In this course, the wheat crop is covered. The same method applies to all other crops. With the advent of remote sensing and GIS technologies, it has become possible to estimate crop yields using various methodologies. Remote sensing is a powerful tool that can be used to identify and classify different crops, assess crop conditions, and estimate crop yields. One of the most popular methods for crop identification using remote sensing is to relate crop NDVI as a function of yield. This method uses various spectral, textural and structural characteristics of crops to classify them using the machine learning method in ArcGIS. Another popular method for crop condition assessment using remote sensing is crop classification then relate to NDVI index. This method uses indices such as NDVI to assess the health of the crop. Both of these methods are widely used for crop identification and assessment. Crop yield estimation can also be done by using remote sensing data. Yield estimation using remote sensing is done by using statistical methods, such as regression analysis and modelling in GIS and excel, including classification and estimation. One popular method for estimating wheat yield is the crop yield estimation model using classified and modelled data with observed records, as shown in this course. This model uses various remote sensing data to estimate the wheat yield. It is also important to validate the developed model on another nearby study area. That validation of the developed model is also covered in this course. The identification of crops is an important step in estimating crop yields and managing agricultural resources. In summary, remote sensing and GIS technologies are widely used for crop identification, crop condition assessment, and crop yield estimation. They provide accurate and timely information that is critical for managing agricultural resources and increasing crop yields. Highlights: Use Machine learning method for crop classification in ArcGIS, separate crops from natural vegetation The model was developed using the minimum observed data available onlineCrop NDVI separationCrop Yield model developmentCrop production calculation from GIS model dataIdentify the low and high-yield zones and area calculationCalculate the total production of the regionValidation of developed model on another study area Validate production and yield of other areas using a developed model of another areaConvert the model to the ArcGIS toolboxYou must know: Basics of GISBasics of ExcelSoftware Requirements: Any version of ArcGIS 10.0 to 10.8Excel...
11. Complete Remote Sensing and GIS - ArcGIS - Erdas
This course covers Basic tasks to do any real-life project. Learning of Basic of any software is different than the basic task that will enable you to do real-life project. After this course, you will be able to do any real life project. After learning any software when we start any project we face many real world data problems. This course covers all the task that are required to handle any type of GIS data. Even non GIS data to GIS. The theory behind tools, how it works. Hands-on tools. Perfect Mosaic in Erdas, satellite data, Digital Elevation Model, Most of the things covered that required for Mast of GIS students, PhD students and Civil Engineers, Irrigation and Hydrology Engineers. If you have missed your practical classes of GIS, then this course is for you. It covers the whole practical syllabus of GIS, even more than it. It also covers Error resolving is software, issue with Satellite data. Compared same task output on different software. This will enable you to do any real life project. Content are decide based on real life problems which my Engineer student faced in field. Sometimes satellite image not provide sharp resolution data then we take Help of Google Earth, How use that Data in GIS. Even how we can use our Good Android Phone to Survey up to 3meter GPS accuracy by calibration its GPS. Later how we can use in GIS. Other than working with Data we need to represent in Best way. A best presentation of Data is considered to be Good work, so I have also covered how to make research ready GIS layouts. How we can improve satellite image improve resolution up to 15 meter and Processing of 10-meter satellite data. Getting Earthwork of Reservoir volume, converting to 3D. Mosaicking of Digital Elevation Model. Getting Drainage and watershed, stream order. Changing the projection of data. Advance labelling using scripts. Handling NetCDF data. Generation annual rainfall map and interpolation of Data. On Other side NDVI is covered. Cutting Study area for the project and deleting bad data from satellite image and vector files using smart tricks covered. How to reference data without latitude longitude and make it usable. Even how to get street level data from online sources and convert to GIS format. Cutting shapes with shapes. Handling attributes and calculation on that. Data conversion between raster and vector of multiple type. Small mini project also shown how to use a combination of tools to do one task. Even to find the right UTM Zone for your area. Also covers Excel data to GIS. Getting lat long, Making Grid, If you missed your GIS practical classes, then it is for you. Covered 90% hands on and 10% theory on basics. It does not matter which version of the software you have. Tool covered applicable to all version of ArcGIS 10.1 or 10.7 or above. Similarly Applicable to all versions of Erdas 2015 or 2018 and above. Try Not to jump between videos, many future video required concept covered in past videos. online courses in gis and remote sensingbasic gis and mapping, basic gis and mapping essentialgis course for beginnersthe basic concept of gisgeospatial analysisReal-life GIS Problems that solved by this courseIn real life GIS project, we face many problems, Like We get data but not from toposheetOr we have the number of Excel sheets that need to convert to map, The study area lies in Two or more satellite images that are totally different. Sometime Station data not available for weather analysis. Counting vegetation in a fraction of second NDVIor, Still typing attribute open by one. Still making road maps by Digitizing, When QGIS helps a lot in combination with ArcGISWant to represent multiple labels in ShapefileMouse control is not good, autocorrection Advanced Editing of Shapefiles. The Best one Presenting work in the Best way so that everyone thinks, you really did Much Hard work. The best way of result presentation like high impact factor journals, Then This course is for you...
12. The Google Earth Engine Mega Course: Remote Sensing & GIS
Welcome to the Google Earth Engine Mega Course: Remote Sensing & GIS, the only course you need to learn to code and become an Earth Engine expert. With a 4.8 average rating, my Earth Engine course is one of the HIGHEST RATED courses. At 12+ hours, this Earth Engine course is without a doubt the most comprehensive Google Earth Engine course available online. Even if you have zero programming experience, this course will take you from beginner to mastery. Here's why: The course is taught by an experienced spatial data scientist and former NASA fellow. The course has been updated to be 2023-ready and you'll be learning the latest tools available on the cloud. The curriculum was developed over a period of four years, with comprehensive student testing and feedback. We've taught over 20,000 students how to code and apply spatial data science and cloud computing. The course is constantly updated with new content, with new projects, and modules. You will have access to example data and sample scripts. In this course, we will cover the following topics: Introduction to Earth Engine JavaScript APIExplore Earth EngineSign Up with Earth EngineBasic JavaScript SyntaxSources of Earth Observation DataLandsat Image VisualizationMathematical Operations with ImagesImage Collection MetadataFiltering Image CollectionMapping Image CollectionReducing Image CollectionsEarth Engine Feature CollectionsEarth Engine GeometriesGeometric OperationsMapping Feature CollectionsReducing Feature CollectionsRaster to Vector ConversionVector to Raster ConversionTime Series ChartsHistogramsExport ImagesImage compositingImage convolutionsImage mosaicingSatellite data summaryRemote sensing for land cover mappingRemote sensing for water resourcesRemote sensing for forest mappingMachine learning with satellite dataThe course includes over 12 hours of HD video tutorials. We'll take you step-by-step through engaging video tutorials and teach you everything you need to know to succeed as a spatial data scientist and Earth Engine expert. So, what are you waiting for? Click the buy now button and join the highest-rated Google Earth Engine course...
13. Remote Sensing in QGIS: Basics of Satellite Image Analysis
Remote Sensing & Satellite Image Analyses in QGIS for BeginnersDo you need to use satellite Remote Sensing in your work but don't know how to apply Remote Sensing analysis?Do you find Remote Sensing books & manuals too not practical and looking for a course that takes you by hand, teach you all the concepts, and get you started on a real-life Remote Sensing analysis project?I'm very excited that you have found my Fundamentals of applied Satellite Remote Sensing in the QGIS course. My course provides you with all the necessary theoretical knowledge and practical skills to implement PRACTICAL, Remote Sensing analysis starting with the basics concepts of Remote Sensing and equipping you with all necessary knowledge and skills to implement your own independent Remote Sensing analysis Project in great QGIS open-source software! I will also demonstrate to you how to implement Remote Sensing analyses in the latest version of open-source software QGIS, thus that you could immediately start using satellite images for your work and Remote Sensing projects. This fundamental 4-hour course is designed to equip you with the theoretical and practical knowledge of applied Remote Sensing analysis. By the end of the course, you will feel confident and completely understand the basics of Remote Sensing and its main components, learn how to install QGIS and work with QGIS and Semi-Automatic Classification Plug-in. You will learn all basics of working with satellite imagery and planning your Remote Sensing project. We will learn in QGIS how to perform image preprocessing, calculate spectral indices, conduct land use and land cover classifications with Machine Learning algorithms, calculate change, and produce GIS maps for your reports and much more. At the end of the course, you will conduct the independent Remote Sensing project-based assignment that will allow you to train your newly acquired geospatial skills! In this course, I will teach you how to work with the popular open-source QGIS software and its great tool, a Semi-Automated classification for Remote Sensing based analysis. The course is ideal for professionals such as geographers, programmers, social scientists, geologists, GIS & Remote Sensing, and all other experts who need to use maps in their field and would like to learn more about geospatial analysis and satellite Remote Sensing in QGIS. One important part of the course is the practical exercises. You will be given some precise instructions, downloadable practical materials, scripts, and datasets to create maps and conduct geospatial analysis using the QGIS software...
14. Remote Sensing for Water Resources in Google Earth Engine
Do you want to apply satellite remote sensing in water resources?Do you want to acquire new hands-on Remote Sensing skills to analyze and process satellite data?Enroll in my new course Remote Sensing for Water Resources in Google Earth Engine. I will provide you with hands-on training with example data, sample scripts, and real-world water resources applications. By taking this course, you will take your geospatial data science skills to the next level by gaining proficiency in satellite remote sensing for water resources application with Google Earth Engine, a cloud-based Earth observation data visualization analysis powered by Google. In this Remote Sensing for Water Resources course, I will help you get up and running on the Google Earth Engine cloud platform to process and analyze geospatial data. By the end of this course, you will be equipped with a set of new GIS and Remote Sensing skills including accessing, downloading processing, analyzing, and visualizing big data using JavaScript programming language with the GEE cloud platform. In this course, I will use real satellite data including Landsat, MODIS, SMAP, TRMM, GPM, and others to provide you with hands-on practical experience of working with Earth observation data. One of the common problems with learning image processing is the high cost of software. In this course, I entirely use the Google Earth Engine JavaScript open-source cloud platform. Additionally, I will walk you through using a step by step video tutorials to process and analyze remote sensing data with GEE. All sample data and scripts will be provided to you as an added bonus throughout the course. Jump in right now to enroll. To get started click the enroll button...
15. Google Earth Engine for Remote Sensing: from Zero to Hero
Complete Google Earth Engine for Remote Sensing MasterclassThis course is designed to take users who use GIS for basic geospatial data/GIS/Remote Sensing analysis to perform geospatial analysis tasks with Big Data on the cloud! This course provides you with all the necessary knowledge to start and advance your skills with Geospatial analysis and includes more than 5 hours of video content, plenty of practical analysis, and downloadable materials. After taking this course, you will be able to implement PRACTICAL, real-life spatial geospatial analysis, and tasks with the Big Data on the cloud. This course is designed to equip you with the theoretical and practical knowledge of applied geospatial analysis, namely Remote Sensing and some Geographic Information Systems (GIS). This course emphasizes the importance of understanding the Google Earth Engine platform and JavaScript to be able to implement spatial analysis on the cloud. So, you will learn: a thorough introduction to the Earth Engine Platform, the basics of image analysis (which is essential to understand when you would like to work with Earth Engine)a comprehensive overview of JavaScript basics for spatial analysis. We will cover essential blocks to equip you with the background knowledge and get you started with your analysis on the cloud. You will learn how to import / export data to Earth Engine, how to perform arithmetical image calculationhow to map functions over image collections, and do iterations. We will cover Sentinel and Landsat image pre-processing and analyses for such applications as drought monitoring, flood mapping, and land cover unsupervised and supervised (machine learning algorithms such as Random Forest) classificationWe finish with an introduction to time series trend analysis in GEE. By the end of the course, you will feel confident and completely understand the basics of JavaScript for spatial analysis and you will learn practical geospatial analysis with Big Data on Google Earth Engine cloud. This course will also prepare you for using geospatial analysis with open source and free software tools. One important part of the course is the practical exercises. You will be given some precise instructions, codes, and datasets to create for geospatial analysis in Google Earth Engine. INCLUDED IN THE COURSE: You will have access to all the data used in the course, along with the Java code files. You will also have access to future resources. Enroll in the course today & take advantage of these special materials!...
16. Remote Sensing for Land Cover Mapping in Google Earth Engine
Do you want to implement a land cover classification algorithm on the cloud?Do you want to quickly gain proficiency in digital image processing and classification?Do you want to become a spatial data scientist?Enroll in this Remote Sensing for Land Cover Mapping in Google Earth Engine course and master land use land cover classification on the cloud. In this course, we will cover the following topics: Unsupervised Classification (Clustering)Training Reference dataSupervised Classification with Landsat Supervised Classification with Sentinel Supervised Classification with MODIS Change Detection Analysis (Water and Forest Change Analysis)Global Land Cover Products (NLCD, Globe Cover, and MODIS Land Cover)I will provide you with hands-on training with example data, sample scripts, and real-world applications. By taking this course, you will take your spatial data science skills to the next level by gaining proficiency in processing satellite data, applying classification algorithms, and assessing classification accuracy using a confusion matrix. We will apply classification using various satellites including Landsat, MODIS, and Sentinel. When you are done with this course, you will master methods on how to apply machine learning and supervised classification algorithm using cloud computing and big geospatial data. Jump in right now to enroll. To get started click the enroll button...
17. Get started with GIS & Remote Sensing in QGIS #Beginners
This course provides an introduction to GIS (geographic information systems) and Remote Sensing for spatial analysis with the emphasis on open source software available for free as well as free spatial data portals that offer a possibility to get started with the GIS, Remote Sensing, and spatial data analysis. This spatial analysis introductory course will provide you with an understanding of the GIS system and Remote Sensing in a very short time. By the end of the course, you will feel confident and completely understand the GIS and Remote Sensing technology and where get GIS software geodata to make maps. This course will prepare the students for the basics of using GIS and Remote Sensing with open source and absolutely free software tools. We will go over various industries where GIS and Remote Sensing can be used including agriculture, geology, mining, hydrology, forestry, environmental, and many more! We will talk about the main GIS components and stages of GIS analysis. I will explain your desktop computer requirements needed to start working with GIS. We will talk about different geodata types. Finally, I will also equip you with the knowledge of different geospatial software tools available and GIS data portals where you can download your spatial maps and data for free. As a BONUS lecture, I provide you a half an hour step-by-step video training on how to quickly create a land cover/ land use map on the cloud with Google Earth Engine using Machine Learning algorithms without any prior knowledge. In this course, I include downloadable practical materials that will teach you:- Understand the fundamentals of GIS and Remote Sensing- Learn a variety of open FREE data sources and GIS % Remote Sensing software for conduction geospatial analysis- How to install open source GIS software on your computer and correctly configure it- QGIS software interface including its main components and plug-ins- Learn how to prepare your first GIS map using open-source tools in QGIS- Learn how to create a land use/land cover map using Google Earth Engine...
18. Machine Learning in GIS and Remote Sensing: 5 Courses in 1
This course is designed to equip you with the theoretical and practical knowledge of Machine Learning and Deep Learning in QGIS and ArcGIS as applied for geospatial analysis, namely Geographic Information Systems (GIS) and Remote Sensing. By the end of the course, you will feel confident and completely understand the Machine and Deep Learning applications in Remote Sensing & GIS technology and how to use Machine and Deep Learning algorithms for various Remote Sensing & GIS tasks, such as land use and land cover mapping (classifications) and object-based image analysis (segmentation, object detection) and regression modeling in QGIS and ArcGIS software. This course will also prepare you for using GIS with open source and free tools (QGIS) and a market-leading software (ArcGIS). This course is designed to take users who use QGIS & ArcGIS for basic geospatial data/GIS/Remote Sensing analysis to perform more advanced geospatial analysis tasks including object-based image analysis using a variety of different data and applying Deep Learning & Machine Learning state of the art algorithms. In addition to making you proficient in QGIS for spatial data analysis, you will be introduced to another powerful processing toolbox - Orfeo Toolbox, and to the exciting capabilities of ArcMap and ArcGIS PRO! In the course, you will be able to apply such Machine Learning algorithms as Random Forest, Support Vector Machines, Decision Trees, Convolutional Neural Networks (and others) for Remote Sensing and geospatial tasks. You will also learn how to conduct regression modeling for GIS tasks in ArcGIS. On top of that, you will practice GIS & Remote Sensing by completing two independent GIS projects by exploring the power of Machine Learning and Deep Learning analysis in QGIS and ArcGIS. This course is different from other training resources. Each lecture seeks to enhance your GIS and Remote Sensing skills in a demonstrable and easy-to-follow manner and provide you with practically implementable solutions. You'll be able to start analyzing spatial data for your projects and gain appreciation from your future employers with your advanced GIS & Remote Sensing skills and knowledge of cutting-edge geospatial methods. The course is ideal for professionals such as geographers, programmers, social scientists, geologists, GIS & Remote Sensing experts, and all other experts who need to use maps in their field and would like to learn more about Machine Learning in GIS. One important part of the course is the practical exercises. You will be given some precise instructions and datasets to create maps based on Machine Learning algorithms using the QGIS and ArcGIS software tools...
19. QGIS Mega Course: GIS and Remote Sensing- Beginner to Expert
QGIS Mega Course: GIS and Remote Sensing - Go from Basic To Expert LevelDo you need to use GIS and spatial analysis in your work but don't know how to apply these analyses and don't know the software to use for GIS-related work?Do you find GIS books and QGIS manuals not practical and looking for a course that takes you by hand, teach you all the concepts, and gets you started on a real-life GIS project?I'm very excited that you have found my QGIS Mega Course: GIS and Remote Sensing - Go from Basic To Expert Level course. I will demonstrate to you how to implement GIS and Remote Sensing analyses in the latest version of open-source software QGIS, thus that you could immediately start using vector and raster data as well satellite images for your work and Remote Sensing projects. In this course, I will teach you how to work with the popular open-source QGIS software and its great tools and plug-ins step-by-step that you can go from Beginner to Expert in GIS analysis in QGIS. Quantum GIS (QGIS) is an open-source Geographic Information System that supports most geospatial vector and raster file types and database formats. The program offers wide GIS functionality, with a variety of mapping features and data editing. This enables cutting-edge, global-scale analysis and visualization. My course provides you with all the necessary theoretical knowledge and practical skills to implement PRACTICAL GIS analysis in QGIS software starting with the basics concepts of GIS and Remote Sensing and equipping you with all the necessary knowledge and skills to implement your own independent GIS and Remote Sensing analysis in great QGIS open-source software! This fundamental 7-hours course is designed to equip you with the theoretical and practical knowledge of applied GIS analysis. By the end of the course, you will:- Learn how to install QGIS and work with QGIS and its plug-ins: Semi-Automatic Classification Plug-in (SCP), TrendsEarth, Google Earth Engine plug-in, SAGA and OTB processing toolboxes etc- Feel confident and completely understand the basics of GIS and Remote Sensing and its main components,- Learn all basics and core GIS data operations (vector and raster data as well as satellite imagery) and learn how to conduct GIS projects.- Learn in QGIS how to perform image preprocessing, calculate spectral indices, conduct land use and land cover classifications with Machine Learning algorithms,- Visualize GIS data and produce GIS maps for your reports- And moreThe course is ideal for professionals such as geographers, programmers, social scientists, geologists, GIS & Remote Sensing, and all other experts who need to use maps in their field and would like to learn more about geospatial analysis and satellite Remote Sensing in QGIS. One important part of the course is the practical exercises. You will be given some precise instructions, downloadable practical materials, scripts, and datasets to create maps and conduct geospatial analysis using the QGIS software. So, don't wait and enrol now to become GIS professional tomorrow!...