Model Selection

Choose the best AI model for your geospatial analysis needs

🏔️

Landslide Detection

Advanced models for detecting and predicting landslide susceptibility using satellite imagery and terrain data

12 Models Avg. Accuracy: 94.2%
🔄

Change Detection

Temporal analysis models for monitoring land cover changes, urban expansion, and environmental shifts

8 Models Avg. Accuracy: 91.7%
🗺️

Land Classification

Comprehensive land use and land cover classification models for various geographical regions

15 Models Avg. Accuracy: 89.3%
✂️

Object Segmentation

Precise object detection and segmentation for infrastructure, vegetation, and geological features

6 Models Avg. Accuracy: 96.1%

Available Models

Advanced Landslide Predictor v2.1

Deep Learning

State-of-the-art CNN model trained on 50K+ satellite images for accurate landslide susceptibility mapping in mountainous terrain.

94.2%
Accuracy
0.91
F1-Score
CNN Satellite Imagery Terrain Analysis Risk Assessment

Temporal Change Detector

Computer Vision

Multi-temporal analysis model for detecting land use changes, urban expansion, and environmental monitoring over time.

91.7%
Accuracy
0.89
IoU Score
Time Series Multi-temporal Urban Planning Environmental

Multi-Class Land Classifier

Random Forest

Comprehensive land cover classification supporting 12 different classes including forests, agriculture, urban areas, and water bodies.

89.3%
Accuracy
12
Classes
Multi-class Land Cover LULC Ensemble

Precision Object Segmenter

U-Net

High-precision semantic segmentation for infrastructure detection, building footprints, and road network extraction.

96.1%
Accuracy
0.94
mIoU
Segmentation Infrastructure Buildings Roads

Forest Health Monitor

CNN + LSTM

Specialized model for forest health assessment, disease detection, and vegetation stress analysis using spectral indices.

92.4%
Accuracy
0.88
Precision
Forest Health Assessment NDVI Disease Detection

Flood Risk Assessor

Ensemble ML

Comprehensive flood risk prediction model combining DEM data, rainfall patterns, and hydrological modeling for early warning systems.

88.7%
Accuracy
0.85
Recall
Flood Risk DEM Hydrology Early Warning