munish0838/crop-burn-detector-v2.1

crop-burn-detector-v2

Fine-tuned LFM2.5-VL-450M (Liquid AI) on the crop-burn-detection-labeled dataset.

Given a pair of Sentinel-2 satellite images (RGB + SWIR), the model outputs a structured JSON burn assessment for a 5 km × 5 km agricultural tile.

Inference

from transformers import AutoProcessor, AutoModelForImageTextToText
from PIL import Image

model = AutoModelForImageTextToText.from_pretrained("munish0838/crop-burn-detector-v2", torch_dtype="bfloat16", device_map="auto")
processor = AutoProcessor.from_pretrained("munish0838/crop-burn-detector-v2")

rgb_image  = Image.open("tile_rgb.png")
swir_image = Image.open("tile_swir.png")

messages = [
    {"role": "system", "content": "You are an expert in analyzing Sentinel-2 satellite imagery for crop residue burning detection in northern India."},
    {"role": "user", "content": [
        {"type": "image", "image": rgb_image},
        {"type": "image", "image": swir_image},
        {"type": "text",  "text": "Analyze this RGB + SWIR satellite tile and return a JSON burn assessment."},
    ]},
]

inputs = processor.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True).to(model.device)
out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
print(processor.batch_decode(out, skip_special_tokens=True)[0])

Output Schema

{
  "burn_detected": true,
  "burn_severity": "moderate",
  "burn_fraction_estimate": 0.25,
  "burn_freshness": "recent",
  "active_smoke_visible": false,
  "vegetation_phase": "post_harvest",
  "image_quality_limited": false,
  "notes": "Dark brownish-red burn scars with rectangular field boundaries visible in SWIR."
}

Training Details

Base model	LiquidAI/LFM2.5-VL-450M
Training data	munish0838/crop-burn-detection-labeled
Train samples	1,098
Epochs	5
Learning rate	0.0001
LoRA rank	32
Effective batch	16