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VijayPatidar-01/README.md

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🌍 About me....

I'm Vijay Patidar, a GeoAI & Remote Sensing Developer from Madhya Pradesh, India 🇮🇳

I work at the intersection of satellite imagery, machine learning, and geospatial analysis — building systems that extract meaningful information from Earth observation data. My work spans SAR analysis, multi-temporal change detection, soil moisture estimation, and land cover classification using data from satellites like Sentinel-1, Sentinel-2, LISS-4, and SMAP.

I'm passionate about solving real-world problems — especially in agriculture and environment — using data that comes from space. 🛰️

🛠️ Technical Skills

👨‍💻 Programming & ML

Python scikit-learn PyTorch NumPy Pandas

🛰️ Remote Sensing & GIS

Google Earth Engine OpenCV GDAL Rasterio GeoPandas

🌐 Backend & Visualization

FastAPI Leaflet Matplotlib Seaborn

🗂️ File Formats I Work With

Format Extension Use
GeoTIFF .tif Raster satellite imagery, classification outputs, DEMs
Shapefile .shp .gpkg Vector AOI boundaries, training samples, field sites
NetCDF .nc IMD gridded rainfall & temperature, climate datasets
CSV / Excel .csv / .xlsx IoT sensor data, feature tables, model results

🛰️ Satellites & Data Sources I Work With

Satellite / Source Agency Domain
Sentinel-1 ESA SAR · C-band backscatter (VV, VH)
Sentinel-2 ESA Multispectral · RGB · NIR · SWIR
LISS-4 ISRO High-res multispectral (5.8m)
SMAP SPL3SMP_E NASA Soil moisture passive microwave
SoilGrids 250m ISRIC Global soil properties
IMD Gridded IMD Daily rainfall & temperature grids

🧠 Core Domains

🛰️  SAR Image Analysis          →  Sentinel-1 VV/VH backscatter processing
🌱  Soil Moisture Estimation     →  Multi-layer prediction (6 depth layers)
🗺️  LULC Classification         →  OBIA + spectral indices + Random Forest
🔍  Change Detection             →  Multi-temporal ML-based land cover analysis
📐  Image Registration           →  Hybrid multi-stage band alignment pipelines
🌿  Spectral Index Computation   →  NDVI · SAVI · NDWI · VV/VH ratio

💡 Non-Technical Skills

Skill Description
📊 Data Visualization Translating complex geospatial outputs into clear, interpretable maps and charts
📝 Research & Report Writing Documenting methodologies, workflows, and results in structured, readable format
🧩 Problem Solving & Analytical Thinking Designing multi-source data pipelines to tackle sparse-data challenges in real-world environments

📬 Let's Connect

Email GitHub

🛰️ Sentinel · 🌍 SMAP · 🌧️ IMD · 🪨 SoilGrids · 🤖 ML · 🖼️ OpenCV · 🗺️ Leaflet

Popular repositories Loading

  1. Google_Earth_Engine_code Google_Earth_Engine_code Public

    1

  2. GeoSpatial_Py GeoSpatial_Py Public

    Jupyter Notebook

  3. LULC-Classification-OBIA LULC-Classification-OBIA Public

    Automate Land Use Land Cover classification using LISS-4 data with Object-Based Image Analysis (OBIA), OTB segmentation and Random Forest model

  4. Soil-Moisture-Prediction-using-Multi-Source-Data-Fusion Soil-Moisture-Prediction-using-Multi-Source-Data-Fusion Public

    Designed a machine learning model for soil moisture estimation using Sentinel-1 SAR backscatter

  5. Satellite-Data-Preprocessing-Pipeline Satellite-Data-Preprocessing-Pipeline Public

    Building a Python-based pipeline to convert raw satellite imagery into analysis-ready datasets.

  6. GeoAI-Multi-Temporal-Satellite-Change-Detection-Tool GeoAI-Multi-Temporal-Satellite-Change-Detection-Tool Public

    Implemented a dynamic Python based change detection tool using Sentinel-2 imagery