This repository is a collection of basic scripts for landslides detection using space-based data in Sentinel Hub. It aims to facilitate working by Big-data in the cloud to attend for a rapid response after a disaster event.
Automatic Landslide Prediction and Rescue Optimization System (ALPROS).
We present MMLSv2, a dataset for landslide segmentation on Martian surfaces. MMLSv2 consists of multimodal imagery with seven bands: RGB, digital elevation model, slope, thermal inertia, and grayscale channels. MMLSv2 comprises 664 images distributed across training, validation, and test splits.
A prototype that detects canopy movement on slopes, which is indicative of landslide risk
This repository contains a workflow for the automatic ranking of landslide candidate areas at a regional scale using EGMS InSAR data for territorial planning and risk management
The proposed system is a hybrid deep-learning architecture for landslide detection and segmentation from satellite, aerial, and drone imagery
A cheap landslide detecting kit made using Arduino , moisture sensors , buzzers and vibration sensor
🌋 Automated landslide detection from satellite imagery using UNet++ EfficientNet-b5. Processes 14-band multispectral data (Sentinel-2 + DEM) for pixel-level segmentation. Test F1=0.6937 on Landslide4Sense benchmark.
A decentralized IoT + Machine Learning system for hyperlocal cloudburst/landslide risk prediction. ESP32 sensor nodes (soil moisture, rainfall, DHT11) transmit over LoRa and WiFi-UDP to a Flask server running a RandomForest classifier, with real-time MySQL logging, a live web dashboard, and Blynk/GSM/buzzer field alerts.
A complete and fast Landslide Susceptibility Tool in MATLAB
Detects landslides and boulders on the Moon from Chandrayaan-2/3 orbital imagery (ISRO BAH-2025 Problem Statement #11)
PyTorch landslide segmentation research prototypes with AMMG-style U-Net, Graph U-Net, transfer learning, and reproducibility tests.
Pyhton implementation of Landslide Hazard Assessment Model (LHASA_V1) for north-east hilly region of Bangladesh
IoT-based landslide prediction system using ESP32 with soil sensors, weather data (ThingSpeak), and Telegram alert system
An IoT-based Landslide Detection and Monitoring System using ESP32, Soil Moisture Sensor, Vibration Sensor, Blynk, Telegram Bot, OLED Display, LEDs, and Buzzer for real-time monitoring and early warning alerts.
This project aims to develop deep learning model (Unet) to detect landslide area from high resolution UAV imagery.
Home-buildable and modular, open-source science station for remote data logging and monitoring.
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