README.md

metadata

title: >-
  DeepSlide - Landslide Detection and Mapping Using Deep Learning Across
  Multi-Source Satellite Data and Geographic Regions
emoji: 🌍
colorFrom: blue
colorTo: green
sdk: docker
app_port: 8501
tags:
  - streamlit
  - pytorch
  - deep-learning
  - landslide-detection
pinned: true
short_description: Landslide Detection using Deep Learning
license: apache-2.0
paper:
  - https://huggingface.co/papers/2507.01123

DeepSlide: Landslide Detection Models

This Space demonstrates various deep learning models for landslide detection, using models trained with PyTorch. The models are served directly from our harshinde/DeepSlide_Models or Kaggle Models Repository.

Available Models

• DeepLabV3+
• DenseNet121
• EfficientNetB0
• InceptionResNetV2
• InceptionV4
• MiT-B1
• MobileNetV2
• ResNet34
• ResNeXt50_32X4D
• SE-ResNet50
• SE-ResNeXt50_32X4D
• SegFormer
• VGG16

How to Use

1. Select a model from the sidebar
2. Upload one or more .h5 files containing satellite imagery
3. View the landslide detection results and predictions
4. Download the results if needed

Model Information

All models are trained on satellite imagery data and are optimized for landslide detection. Each model has its own strengths and characteristics, which are described in the app interface when you select them.

Technical Details

• Python 3.9
• PyTorch 1.9.0
• Streamlit 1.28.0
• Models are automatically downloaded from HuggingFace harshinde/DeepSlide_Models.
• Dataset harshinde/LandSlide4Sense.

Author

• Harsh Shinde

DeepSlide: Landslide Detection and Mapping Using Deep Learning Across Multi-Source Satellite Data and Geographic Regions

Landslide4sense Dataset:

from datasets import load_dataset

ds = load_dataset("harshinde/LandSlide4Sense")

Deepslide Models - harshinde/DeepSlide_Models

Dataset harshinde/LandSlide4Sense

Wandb Results - https://wandb.ai/Silvamillion/Land4Sense

Paper - https://dx.doi.org/10.2139/ssrn.5225437

📄 Citation

If you use DeepSlide-L4S-Code or reference our work in your research, please cite our paper:

Harsh Shinde, et al. Landslide Detection and Mapping Using Deep Learning Across Multi-Source Satellite Data and Geographic Regions, SSRN, 2024.
DOI: 10.2139/ssrn.5225437

BibTeX:

@article{burange2025landslide,
  title={Landslide Detection and Mapping Using Deep Learning Across Multi-Source Satellite Data and Geographic Regions},
  author={Burange, Rahul and Shinde, Harsh and Mutyalwar, Omkar},
  journal={Available at SSRN 5225437},
  year={2025}
  eprint={5225437},
  archivePrefix={SSRN},
  doi={10.2139/ssrn.5225437},
  url={https://dx.doi.org/10.2139/ssrn.5225437}
}