About
Who built this.
Dr. Anant Kumar Pathak — PhD, former Assistant Professor, working in GIS and spatial analysis.
LinkedInSpatial Research Suite is a research tool for people who work with satellite imagery: extract administrative boundaries, classify land cover with Random Forest or CART, detect statistically significant trends, and export a publication-ready map — without installing desktop GIS software or writing Earth Engine code.
Set a study area once. Every tool below works from that same boundary, so you're not re-uploading shapefiles between steps.
Train Random Forest, CART, or SVM classifiers on your own ground-truth points. Includes per-pixel confidence mapping and a held-out validation accuracy report — not just training accuracy.
Fit a per-pixel linear trend across any year range, with a Mann-Kendall significance test to separate real change from noise. Harmonized Landsat 5/7/8/9 data extends coverage back to 1984.
Auto-generated transition matrices and a spatial change-detection map, with minimum mapping unit filtering to remove classification noise between two dates.
Run zonal statistics across dozens of boundaries in a single server-side call. Test correlations between any two variables — vegetation and temperature, for example — with a scatter plot and Pearson's r.
The same four steps whether you're running a quick NDVI check or a full classification.
Upload a shapefile, draw a boundary directly on the map, or extract one from built-in administrative datasets (India down to village level, or any country globally).
Pick from vegetation health, surface temperature, precipitation, nighttime lights, and a dozen other Earth Engine datasets — or label training points and train a land-cover classifier.
Every result comes with the numbers behind it: accuracy and kappa on held-out test data, Mann-Kendall significance, confidence intervals — not just a colored map.
Download a cartographic map with a north arrow, scale bar, and legend, plus an auto-generated methodology report listing exact datasets, parameters, and citations.
Reproducible methodology reports and defensible accuracy metrics, for work that needs to hold up in peer review.
Track land-use change and urban heat over time across a district or state, without commissioning a custom analysis.
Batch-process zonal statistics across many boundaries at once, and export client-ready cartography directly.
Run a full classification workflow — training, validation, accuracy assessment — without setting up a Python or GEE development environment.
Specifics that matter if you're going to cite this in a report or a paper.
No subscription. No account required. Two ways to pay, processed securely through Razorpay.
Download a single map, chart, or data table. Pay only for the specific result you need.
Unlimited downloads for the rest of your current session — worthwhile once you need more than two exports.
No. The interface walks you through each step (set a boundary, pick a feature or train a classifier, review results). That said, the outputs — accuracy metrics, significance tests, confidence intervals — are built for people who'll recognize and use them, so a research or planning background helps you get the most out of it.
Accuracy and kappa are computed on a held-out test split (30% of your training points, never seen during training) whenever you provide at least 20 points — not on the same data the classifier trained on, which is a common shortcut that inflates the number. Below that threshold, the report tells you explicitly that you're seeing training-data accuracy, not held-out validation.
Boundary and shapefile data you upload or draw lives only in your active browser session — it's used to run the analysis you request and isn't permanently stored or shared with third parties. See the Privacy Policy for details.
Landsat 5, 7, 8, and 9 (harmonized back to 1984), Sentinel-2, CHIRPS precipitation, SRTM elevation, VIIRS nighttime lights, and WorldPop population density — all accessed through Google Earth Engine's public data catalog.
Yes — every export includes an auto-generated methodology note listing the exact datasets, date ranges, spatial resolution, and classifier parameters used, plus citations for the underlying data sources, so your methodology section is reproducible.
No. Every payment is one-time — either for a single export or for unlimited downloads within your current session. Nothing recurs, and no account is required.
Dr. Anant Kumar Pathak — PhD, former Assistant Professor, working in GIS and spatial analysis.
LinkedInLast updated:
Spatial Research Suite has no user accounts or login system — you use it anonymously in a browser session.
This Service relies on Google Earth Engine and Razorpay. Each operates under its own privacy policy for the data it directly handles.
Session data is cleared when your browser session ends. We do not maintain a database of your past analyses tied to your identity.
Questions: anant4infinity@gmail.com
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The Service provides geospatial analysis tools built on Google Earth Engine. Some features are free; others require a one-time or session-based payment via Razorpay.
Analysis results are provided "as is" for research and informational purposes. Satellite-derived results carry inherent uncertainty. You are responsible for independently verifying results before using them for decisions with legal, financial, or safety consequences.
To the maximum extent permitted by law, the Service and its creator are not liable for indirect, incidental, or consequential damages arising from use of the Service or reliance on its outputs.
Questions: anant4infinity@gmail.com
Last updated:
Purchases are one-time payments for a single downloadable asset or unlimited access for the remainder of your current session — digital services delivered immediately upon successful payment.
Email anant4infinity@gmail.com with your Razorpay payment ID. Eligible refunds are processed within 5-7 business days.