Grid Mapper - Substation Detector
Map electricity-grid assets and detect candidate substations, including resumable country-scale segment scanning, human-reviewed continual learning and safe model versioning.
Grid Mapper turns the MSc Data Science workflow "Investigating the use of Deep learning tools to Map substations in Kenya" (University of East London, 2023) into QGIS Processing tools. It maps grid assets from OpenStreetMap, optionally detects candidate substations from imagery, captures human-reviewed TP/FP/corrected/missed examples, exports fixed train/validation/test snapshots, and maintains a local model registry with conservative promotion gates. The default Map the grid mode is OpenStreetMap-only and needs no AI runtime or model. Optional local ONNX inference requires the external Python package onnxruntime. Optional TFLite inference requires ai-edge-litert, tflite-runtime or tensorflow. These runtimes and model weights are not bundled. Roboflow mode requires a Roboflow API key and uploads image tiles to Roboflow for inference. Grid Mapper also contacts OpenStreetMap Nominatim/Overpass and whichever imagery service the user selects. See the README and docs/NETWORK_AND_PRIVACY.md before using third-party services or imagery commercially.