Large-Scale Early / In-Season Crop Type Mapping

Final Year Project — MachVis Lab (NUST) in collaboration with NARC & DAAD, Germany Awarded: Flagship Project

This repository contains the complete framework, preprocessing pipeline, model code, and scripts used in my Final Year Project on large-scale crop type mapping across Punjab and Sindh using satellite imagery.

📄 Publication & Links

• Final Report

• Poster

• Frontend

• Publication — Manuscript in Preparation.

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The project focuses on operational, in-season crop monitoring at 30m resolution using multispectral imagery and state-of-the-art ViT-based semantic segmentation models. All results—including provincial maps, district crops, stitched tiles, JSON outputs, and RGB renderings—are generated using this framework.

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🚀 Project Summary

• Developed a large-scale, end-to-end pipeline for classifying crops across Pakistan using 20GB+ of seasonal satellite data.
• Implemented domain adaptation between US → Uzbekistan → Pakistan, addressing the fundamental challenge of limited labeled data in Pakistan.
• Fine-tuned Satlas-Pretrain Vision Transformer (ViT) segmentation head for 5 classes: Cotton, Wheat, Urban, Natural, Others
• Achieved state-of-the-art accuracy: 79.3% Overall Accuracy.
• Deployed a scalable inference pipeline capable of generating full-province crop maps.
• Produced high-resolution district PNGs, stitched tiles, JSON summaries, and statistical outputs.

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🌍 Key Outputs

The framework generates:

• Seasonal crop maps for Punjab & Sindh
• District-level cropped PNGs
• Tile-wise stitched TIFs/PNGs
• JSON summaries of crop distributions
• Province-wide composite maps
• RGB visualizations for inspection

Example seasons included in this repository:

• Jan-Apr 2025
• Jun-Dec 2024

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📁 Repository Structure

A simplified overview of the repository (excluding large datasets):

├── api/
│   ├── model/
│   └── routes/
│       └── map.py
├── model/
│   ├── model.py
│   └── unet_best.pth
├── dataloader.py
├── createMasks.py
├── createOutputMap.py
├── createLargeOutputMap.py
├── downloadTileEarthAccess.py
├── patchifyTileForPrithvi.py
├── stitch256masks.py
├── tiffToCroppedPngs.py
├── tiffToPunjabPng.py
├── tiffToSindhPng.py
├── viz.py / viz2.py
├── main.py
├── util/
├── bandspng/                  # 16-band inputs visualized
├── mapdata/                   # District outputs, stitched tiles, JSON stats
├── requirements.txt
└── readme.md

Note: Large imagery, stitched tiles, and JSON outputs are included for reference but not needed for inference if using your own data.

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🧠 Model Details

✦ Backbone

Satlas-Pretrain Vision Transformer (ViT) — pretrained on global multi-sensor satellite imagery.

✦ Fine-tuning Setup

• 30m spatial resolution
• 16 multispectral bands
• 5-way semantic segmentation
• Optimized loss for highly imbalanced crop distribution

✦ Target Classes

Label	Class
0	Other
1	Cotton
2	Wheat
3	Urban
4	Natural

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🔧 Processing & Inference Pipeline

The workflow consists of:

1. Tile Downloading downloadTileEarthAccess.py

2. Patchification patchifyTileForPrithvi.py

3. Model Inference main.py (Loads unet_best.pth, runs inference tile-by-tile)

4. Mask Generation createMasks.py

5. Stitching Outputs stitch256masks.py → Produces district-level and province-level maps

6. JSON Summary Generation viz.py, viz2.py → Extracts per-class stats

7. Cropped PNG Generation tiffToPunjabPng.py, tiffToSindhPng.py

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▶️ Running Inference

Install dependencies:

pip install -r requirements.txt

Run inference on satellite tiles:

python main.py --input <tile_folder> --output <output_folder>

Generate stitched province outputs:

python createLargeOutputMap.py

Create district PNGs:

python tiffToCroppedPngs.py

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📊 Visualization Examples

The repo includes:

• bandspng/ — 16-band visualizations
• stitched_tile_*.png — stitched seasonal outputs
• jsonData/ — crop statistics per district
• croppedPngs/ — final export-ready visual maps

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🌐 Collaboration

This project is part of a research collaboration between:

• MachVis Lab, NUST https://vision.seecs.edu.pk/
• NARC — National Agricultural Research Centre, Pakistan
• DAAD — German Academic Exchange Service

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📬 Contact

For questions, collaborations, or dataset access:

Muhammad Umer Khan Shalina Riaz Syed Hashir Ahmad Kazmi

Contact mukhan.bscs21seecs@seecs.edu.pk MachVis Lab, NUST