Detecting Base Transceiver Stations on Satellite Images using Deep Convolutional Neural Networks

Goal: The objective of this study is to verify locations of base transceiver stations (BTS) in a particular region by utilizing a deep neural network with which to classify geo-referenced image tiles for presence or non-presence of BTS.

This code base allows the reader to replicate the results of this study.

Programming Language: Python 3.6 (can be modified for Python 2.x)

Required Packages: Pillow, overpass, numpy, scipy

For questions and bug reports, please email Aldre Jota at aldrejota[at]gmail.com.

Neural Network Model

Accuracy on Hold-out Sample (Test Set): 96.57%

The architecture of the neural network is similar to the base layers of VGG16* (Simonyan & Zisserman, 2014) topped with a single 256-node fully-connected layer regularized with 50% dropout.

Optimized using stochastic gradient descent with 0.0001 learning rate and 0.9 momentum.

You can access the trained model here.

*Simonyan & Zisserman, 2014, Very Deep Convolutional Networks for Large-Scale Image Recognition [Arxiv]

Instructions for Generating Training Data

1. Generate an API key for Google Static Maps API (free).
2. Select areas of interest in OpenStreetMap (OSM) and create a file listing the area boundary IDs delimited by new line. Note: keep the boundaries small for faster searches; no continent-level boundaries.
3. Install required Python packages:

   pip install -r requirements.txt
   

4. Run the scripts scrape_bts.py and scrape_non-bts.py with inputs from (1) and (2) .
   • The scripts locate coordinates in OSM with tags for BTS (communication towers, mobile communications) and Non-BTS (highways, residential lands, bridges, parking spaces, natural water, etc).
   • Then, the scripts download image tiles from Google Static Maps specified by the latitude and longitude coordinates to the data/bts_images/[area] directory.
5. Important: Manually filter images for inconsistencies and mistags.

Instructions for Building Classifier

1. Check out the application examples from the Keras website.
2. Download the keras_model.json from this repository to recreate the architecture.

json_model = open('keras_model.json', 'r').read()
BTS_model = model_from_json(json_model)

3. "Transfer learning" approach: use the pretrained model of the original VGG16 for the initial weight of the base layers.