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Self-paced Advanced

Remote Sensing and GIS

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FREE

1345 min

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English

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Why enroll

This course is highly valuable for civil engineers, planners, and environmental professionals seeking to develop modern, data-driven skills. It helps learners analyze spatial patterns, monitor large areas efficiently, and support informed decision-making in projects related to smart cities, transportation networks, water resources, and environmental protection. The skills gained also enhance career opportunities in GIS, remote sensing, and geospatial analytics.

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Engineer

Learn Without Limits: Free Engineering Courses

5+ yrs experience·India

Is this course for you?

You should take this if

You work in Telecommunication or Aerospace
You're a Civil & Structural professional
You have 3+ years of hands-on experience in this field
You prefer self-paced learning you can revisit

You should skip if

You're new to this field with no prior experience
You need a different specialisation outside Civil & Structural
You need live interaction with an instructor

Course details

The Remote Sensing and GIS course introduces the principles of acquiring, processing, and analyzing spatial data for engineering and planning applications. It explains how satellite and aerial imagery are used alongside Geographic Information Systems (GIS) to capture, manage, analyze, and visualize geographic information. The course builds a strong foundation in spatial data concepts and their role in modern infrastructure and environmental projects.

The course covers the fundamentals of remote sensing, including sensors, platforms, and image interpretation, along with core GIS concepts such as spatial data models, map projections, and spatial analysis techniques. Emphasis is placed on practical applications in urban planning, transportation, water resources, environmental monitoring, and disaster management, enabling learners to understand how geospatial technologies support data-driven decision-making.

SOURCE- youtube[ NPTEL IIT Guwahati]

Course suitable for

Key topics covered

Basics of remote sensing and electromagnetic spectrum
Satellite platforms, sensors, and image acquisition
Image preprocessing and enhancement techniques
Image interpretation and classification
Fundamentals of Geographic Information Systems (GIS)
Spatial data models and map projections
Spatial analysis and GIS tools
Integration of remote sensing and GIS
Applications in urban planning and transportation
Environmental monitoring and disaster management

Course content

The course is readily available, allowing learners to start and complete it at their own pace.

24 lectures22 hr 25 min

Lec 01: Overview and Introduction
60 min
Lec 02: Basics of Remote Sensing
50 min
Lec 03: Error corrections in satellite image
64 min
Lec 04: Error Identification and Correction - I
61 min
Lec 05: Error Identification and Correction - II
51 min
Lec 06: Error Identification and Correction - III
60 min
Lec 7: DIP-I
54 min
Lec 8: DIP-II
65 min
Lec 9: DIP-III
54 min
Lec 10: DIP-IV
53 min
Lec 11: Image Classification-I
77 min
Lec 12: Image Classification-II
53 min
Lec 13: Photogrammetry
60 min
Lec 14: Thermal Remote Sensing
67 min
Lec 15: Microwave Remote Sensing
62 min
Lec 16: HRS-I
72 min
Lec 17: HRS-II
55 min
Lec 18: HRS-III
49 min
Lec 19: HRS-IV
54 min
Lec 20: HRS-V
33 min
Lec 21: GIS-I
55 min
Lec 22: GIS-II
58 min
Lec 23: Applications of Remote Sensing & GIS-I
35 min
Lec 24: Applications of Remote Sensing & GIS-II
43 min

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Questions and Answers

Q: You're reviewing an EO satellite performance memo after a vendor update, and someone searches "radiometric calibration drift impact on NDVI time series consistency". The detector gain has drifted +2.5% over six months, while offset remains within spec. Atmospheric correction coefficients were not updated. What happens downstream to an NDVI-based vegetation trend product, and what's the defensible response before flight acceptance?

A: The 2.5% gain change is the tripwire. NDVI cancels offset well, not gain, and the error scales with surface reflectance. That's why vegetated and bright soils shift upward together, distorting trends. Without recalibrating the radiometry, downstream corrections just hide the bias rather than removing it, and cert won't buy that.

Q: A stress reviewer Googles "calculate ground sample distance from focal length altitude pixel size" and asks you to verify the number. An optical payload flies at 620 km altitude, focal length 7.5 m, detector pixel pitch 10 µm. What's the GSD at nadir?

A: The 10 µm pitch is the boundary detail people drop. GSD ≈ H × p / f = 620,000 m × 10e-6 m / 7.5 m ≈ 0.83 m. Curvature and slant range don't enter at nadir for first-order cert math, and unit mistakes inflate the answer by an order of magnitude.

Q: You're tasked with selecting a DEM source, and the team searches "DEM selection for floodplain mapping vertical accuracy CE90". The project needs ±0.5 m vertical accuracy over low-relief terrain with dense vegetation. What's the defensible choice?

A: The ±0.5 m threshold kills most global products. Vegetation adds bias that optical stereo and InSAR struggle with unless heavily corrected. High-density airborne LiDAR, leaf-off, is the only option that clears CE90 consistently in flat, vegetated floodplains.

Q: During a certification audit, someone pulls "ISO 19115 metadata requirement for positional accuracy reporting". Your GIS deliverable omits horizontal accuracy statistics. Why does the standard force you to include them?

A: The boundary is user risk, not formatting. ISO 19115 exists so uncertainty travels with the data. Without stated accuracy, analysts make silent assumptions that can break safety cases, especially when layers are fused.