Buildings' Footprints Map

The first comprehensive Lebanese Building Footprints map was autonomously generated using Deep Learning Models that were developed and tested at the Lebanese National Remote Sensing Center - CNRS. We trained fully convolutional ‘Encoder-Decoder’ like Neural Networks on GEOEYE-1 high resolution satellite images (50 cm/pixel) from the Year 2013 for semantic segmentation of buildings' footprint.

When you ZOOM IN, the dots on the map refer to the centroids of each building at a specific geographical location. The map can be accessed via the following link.

The following research manuscripts are focused on urban features extraction from aerial images:

1. Post-War Building Damage Detection
2. Autonomous Building Detection Using Edge Properties and Image Color Invariants
3. Autonomous Vehicle Detection and Classification in High Resolution Satellite Imagery
4. Building shadow detection based on multi-thresholding segmentation