btjones-me / osm-ai-tools

OSM AI Tools

Tools for validating and detecting arbitrary objects defined by OSM tags using high-resolution Mapbox imagery.

Installation

git clone [e-mail]:joconnor-ml/osm-ai-tools.git
cd osm-ai-tools
virtualenv --python=python3.8 venv
venv/bin/activate
pip install -r requirements.txt
pip install -e .  # for 'click' command line tools

Data Pipeline

Note: A Mapbox API token is required to download images. Simplest is using the MAPBOX_ACCESS_TOKEN environment variable.

Given a set of OSM tags that define a type of object, we can build a training set of images (Mapbox) and bounding boxes (OSM) that can be used to train object detection and classification models.

See e.g. examples/cooling_towers/data_pipeline.sh

• generate_object_location_data pulls location data using Overpass
• cluster_objects can be used to group objects into colocated clusters to reduce the number of image requests needed.
• download_images pulls one image per cluster.
• generate_bboxes generates normalised bounding boxes for each object, linking objects and images.

Image Classification for Tag Validation

Raw OSM annotations are often inconsistent, outdated or wrong. In mistag_classification.ipynb we use a cross-validated image classification pipeline to generate a list of likely mistagged objects. Objects with a large discrepancy between their out-of-sample class prediction and their label are likely to be mislabelled.

• Generate positives from images and bounding boxes.
• Generate negatives from random crops. This method relies on images being significantly larger than images to keep mislabelled negatives to a minimum.
• Cross-validation, finetuned RESISC-45 ResNet-50.
• Hand-label candidate mistags (objects with pred_class != true_class)
• Prune mistags from data, retrain model and save.

We boost the performance of classification models using the RESISC-45 aerial imagery dataset for pretraining. See train_resisc45_resnet.ipynb.

TO DO

• Tag Validation API
• Object Detection
• Add TPU Compatibility