AI/ML Forest Species Identification via Remote Sensing
Budget / Salary₹12,500–37,500
TypeFreelance project
LocationRemote
Posted1 hour ago
Remote Sensing + AI/ML Model for Identification of 10 Major NTFP Species in Jharkhand, India
We are looking for an experienced "Remote Sensing / Computer Vision / Geospatial AI developer" to develop or fine-tune an AI/ML model capable of identifying and mapping "10 major Non-Timber Forest Product (NTFP) tree species in Jharkhand, India" using remote sensing imagery.
Target Species
1. Sal
2. Mahua
3. Kusum
4. Tamarind
5. Kendu
6. Palash
7. Chironji
8. Amla
9. Harra
10. Bahera
Objective
The objective is to develop a reliable workflow that can identify these species from remote sensing data and ultimately generate a **species-wise tree inventory/map**, including tree locations and counts.
Scope of Work
We are open to either:
Developing a new model from scratch or
Fine-tuning/adapting an appropriate open-source model such as DeepForest or other tree detection/segmentation and species-classification frameworks.
The expected workflow may include:
Remote Sensing Imagery → Individual Tree/Crown Detection → Feature Extraction → Species Classification → GIS Species Map & Tree Count
Potential data sources may include:
* High-resolution satellite imagery
* Multispectral imagery
* Sentinel-2 time-series data
* Drone imagery, where required
* Ground-truth/GPS data from field surveys
The model should ideally make use of **spectral, temporal, textural and/or crown structural features** where appropriate.
Expected Deliverables
* Working AI/ML model and complete source code
* Pre-processing and training pipeline
* Individual tree/crown detection or segmentation
* Classification of the 10 target species
* Species-wise tree count
* GIS-ready output (Shapefile/GeoJSON/GeoPackage or equivalent)
* Confidence/probability score for each prediction
* Accuracy assessment and confusion matrix
* Documentation explaining the methodology, training process and inference workflow
* Recommendations for scaling the model to larger areas of Jharkhand
Ideal Candidate
Please apply if you have demonstrated experience in:
* Remote sensing and satellite image processing
* Computer vision / deep learning
* Tree detection and crown segmentation
* Tree species classification
* GIS / GeoPandas / Rasterio / Google Earth Engine
* Python, PyTorch/TensorFlow
* Models such as DeepForest, YOLO, Mask R-CNN, U-Net, SAM or similar
* Multispectral/hyperspectral imagery
* Geospatial AI
Experience with forest/tree species mapping is highly preferred.
We are looking for an experienced "Remote Sensing / Computer Vision / Geospatial AI developer" to develop or fine-tune an AI/ML model capable of identifying and mapping "10 major Non-Timber Forest Product (NTFP) tree species in Jharkhand, India" using remote sensing imagery.
Target Species
1. Sal
2. Mahua
3. Kusum
4. Tamarind
5. Kendu
6. Palash
7. Chironji
8. Amla
9. Harra
10. Bahera
Objective
The objective is to develop a reliable workflow that can identify these species from remote sensing data and ultimately generate a **species-wise tree inventory/map**, including tree locations and counts.
Scope of Work
We are open to either:
Developing a new model from scratch or
Fine-tuning/adapting an appropriate open-source model such as DeepForest or other tree detection/segmentation and species-classification frameworks.
The expected workflow may include:
Remote Sensing Imagery → Individual Tree/Crown Detection → Feature Extraction → Species Classification → GIS Species Map & Tree Count
Potential data sources may include:
* High-resolution satellite imagery
* Multispectral imagery
* Sentinel-2 time-series data
* Drone imagery, where required
* Ground-truth/GPS data from field surveys
The model should ideally make use of **spectral, temporal, textural and/or crown structural features** where appropriate.
Expected Deliverables
* Working AI/ML model and complete source code
* Pre-processing and training pipeline
* Individual tree/crown detection or segmentation
* Classification of the 10 target species
* Species-wise tree count
* GIS-ready output (Shapefile/GeoJSON/GeoPackage or equivalent)
* Confidence/probability score for each prediction
* Accuracy assessment and confusion matrix
* Documentation explaining the methodology, training process and inference workflow
* Recommendations for scaling the model to larger areas of Jharkhand
Ideal Candidate
Please apply if you have demonstrated experience in:
* Remote sensing and satellite image processing
* Computer vision / deep learning
* Tree detection and crown segmentation
* Tree species classification
* GIS / GeoPandas / Rasterio / Google Earth Engine
* Python, PyTorch/TensorFlow
* Models such as DeepForest, YOLO, Mask R-CNN, U-Net, SAM or similar
* Multispectral/hyperspectral imagery
* Geospatial AI
Experience with forest/tree species mapping is highly preferred.
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