Get Updated about GIS

List of GIS Projects

List of GIS Topics for M.Tech or Ph.D Project

  1. Land use and Land cover analysis and Change detection. Even future Prediction.
  2. Change analysis of cost line using past data of satellites and find relation between climate change sea level rise.
  3. Impact of Climate change on Groundwater.
  4. Analysis of Groundwater Potential Zones using Remote Sensing and GIS, Using Geology, soil, strata chart, DEM data. Total 22 Layers approx. to run analysis.
  5. Network Mapping, Shortest route, Digital database of route for GPS tracking Device.
  6. Development of GIS Data Hosting Server.
  7. Flood Risk mapping, Identification of Danger zones and damage.
  8. Identification of Hydropower Sites Using DEM and model development for different criteria.
  9. Mapping of Normal and Abnormal route of cyclone and its damage assessment using past data and future prediction.
  10. Soil Erosion and Sediment Modelling using GIS
  11. Identification sites for solar power plant using solar radiation analysis and cloud cover analysis considering earth curvature.
  12. Identification of Minerals using Erdas Material Mapping, e.g. amount of Iron, Zinc in Soil.
  13. Develop a web application using ArcGIS Engine/Server
  14. Required Software :- ArcGIS , Erdas,ENVI, IDRISI 
  15. Estimate of crop yield of different crops by relating NDVI crop Health Index over a Study area and develop a suitable model. It considers time series data for different crop and different season.
  16. City Heat Centre mapping Using Thermal Data and also thermal mapping for different seasons and mark safe and unsafe zones when Air temperature reaches upper limit.
  17. Natural Forest loss Mapping by relation climate change data
  18. Impact of Thermal Power Plant on Surrounding Environment. It uses Wind Direction, Temperature data, Land use data Before Thermal Plant and after thermal plant and compare surrounding environment temperature NDVI, Land Changes etc.
  19. Perform Watershed analysis of and area, find runoff, Drainage Density, Suitability of area for irrigation project, Risk and safe zone mapping by comparing soil data and rainfall data.
  20. Glacial Melt analysis and its factor, e.g. change of snowline with time, and Prediction when it melts fully with suitable Mathematical model.
  21. Mapping of Humidity by collecting Ground data, for different month and its impact on different crops and vegetation.
  22. Traffic jam analysis using GIS, using traffic data, mark locations on road where possibility of traffic jam is possible if n/Number of vehicle cross per minute, and suggest suitable suggestion or development of new rout and its path for prevent jam. 
  23. Watershed simulation and discharge estimation it use ArcSWAT 
  24. Identification of best places for construction of DAM in Mountain region. This use Swat Model, Dem, Rainfall data and Multi-watershed. 
  25. Identification of heat land in urban area. This use thermal band of throughout year. For summer and winter, even each month. At last you will be able to draw annual average surface temperature map and classify results. 
  26. Impact of climate change on Water flow. This use ArcSWAT and climate data, Data of IMD.
Last Update April  2018

Link to GIS Tutorials 

Sometime barren land has same appeared as urban area, Rivers are dry that appears as barren land. Even after final land use we think to add new class. But cannot add. Sometime forest in Hill shade area and appear black. Learn this all Everything how does with New Methods. More than Supervised classification and Get 90% Accuracy. Also learn how to do change detection. Like if urban area is increasing the how much area it takes from Agriculture or Forest, How Landuse is changing w.r.t Each class. Calculate are in Km, or Pixels. Lastly do accuracy report. Learn this everything. Only for LinkedIn offer who reading this post. This is already discounted link. Lear at your time. No time bound. Enrol now and watch more than 5 hrs step and your time.  You will also get valid certificate also.


ArcSWAT is an watershed simulation model. Used for watershed, Water resource planning, Planning of Hydropower projects. It typical model to run with ArcGIS , Most of people facing error in SWAT Model. So, I have created a complete unofficial tutorial of SWAT model with ArcGIS. In this I covered Data download to final results. This include data preparation of SWAT model in Depth. Configuring swat model from scratch. I demonstrated using A live study area, from zero. I also covered custom data set for other countries to prepare for which no data is available, including preparation of soil maps. I covered to remove common error in weather data also. How to read output. How to manual configure inside SWAT model.


n this course you will see Machine learning in Action using readymade land Change model Terrset (formerly IDRISI ) . This course used Terrset Software with CA Markov method to predict future landuse ArcGIS is used to prepare data. Erdas also used for some task. No coding is used .All software used in this course are NOT Open Source. You need to manage software. You must know to prepare landuse maps rest of things covered in this course from scratch. Future prediction of landuse depends on number of drivers/Parameters. Drives means forces which decide how the future urban area will look. It includes many drives like, old city boundary because new settlement will be constructed near to old city boundary. Roads and relief are also one of factors, because first roads near city covered by settlement. On another side how, much possibility at different location on agriculture site that can be convert to urban. Similarly, forest cover also. We also need to avoid some landuse classed like water, river, lake or reservoir never convert to urban. So, we need to setup our model in such a way so that it avoids water. After setting accuracy of learning and output accuracy also matters. We also need to modify it. In this course we have achieved learning accuracy of 42%, and 67% in two different runs. But 89% accuracy we have achieved in predicted landuse. Learning and prediction accuracy is different on computer to computer and data to data. While running you will receive more or less accuracy then this course. But focus on your output results. If Learning accuracy was 100% then it also wrong. So, see and understand each video carefully. Then run you model. You must see free preview video before enrolling this course. Because this is Expert level course.
 Note: Who having IDRISI Taiga They can also follow same steps.


 is and very easy work. Even Just basic knowledge of GIS is Required. You Just need to know to concept how it works and how to take that work from form GIS data. Groundwater Potential zone is just an estimate of possible location of water availability. But it does not tell the water depth. But if you want to know monitor changes in water depth in this case you need to work with actual data and plot that data in GIS. You can also calculate water level changes and location by using GIS if you have observation data. Groundwater Required few layers like Land Use, Rainfall, Soil, Drainage density,  DEM, Even number of layers is not limited you can use any number of layer as per information available, like Geomorphology also. This project is sometime given to Master of PhD students. But actually this is work of Just one day if you have good internet connection and ability to work Just 8 hrs continuous. Only write up will take time. So here is step by step Video tutorial is available for this project. It covers from data downloading to final results, Even the same tutorial covers Drought and Flood risk zone also, because it use same data.  High Quality GIS contents are not free, But too cheap also as 10$. If you learn GIS offline this will cost you thousand of dollars and at last you not have single video of course. You purely depend on you notebook. But if everything is available in video you can do it better, watch any step anytime rewind it. So All reading this post have access to this course using special link. Below this post. 













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