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Aral Basin Environmental Platform
January 1, 2026 • [in-progress]
First modern ML pipeline for the Aral Sea disaster zone.
Tags: environment, ai, satellite, agriculture, water, central-asia, research
50+ years of satellite data - Landsat (1972-present), Sentinel-1/2, MODIS, VIIRS, GRACE-FO gravimetry, ERA5, MERRA-2, and 150+ years of Uzhydromet records from 87 synoptic stations - all analyzed with methods from 2015 or earlier. Random Forest, SVM, Markov-Cellular Automata, traditional indices. No foundation models. No vision transformers. No deep learning. One DL paper exists (2023, Korean group, radiometric normalization only).
Fine-tuning Prithvi-EO-2.0 (NASA/IBM's 600M param geospatial foundation model) and Chronos-2 for six application modules: dust storm early warning, irrigation optimization (deep RL on AquaCrop), water allocation optimization, crop yield prediction, salinity risk mapping, crop disease detection.
Targeting 200+ dust alert users and 10+ farmer pilots in Karakalpakstan by mid-2026. 2-3 papers planned.
Status: In development (data pipeline and model fine-tuning phase)
Stack: Google Earth Engine, Prithvi-EO-2.0, Chronos-2, PyTorch, AquaCrop-OSPy, Gymnasium, SAC, FastAPI, Telegram Bot API
Data: Sentinel-1/2, Landsat, MODIS, VIIRS, GRACE-FO, ERA5, MERRA-2, CA-discharge, Uzhydromet archives
Needs GPU compute for foundation model fine-tuning. Looking for sponsorship.