# kootenpv/whereami

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_How this analysis was created: the description and tags below were written by an AI model that read this project's README and public documentation pages; stars, license and language come straight from the GitHub API. The model does not read the source code._

5,137 stars · 252 forks · Python · AGPL-3.0

## Links

- GitHub: https://github.com/kootenpv/whereami
- awesome-repositories: https://awesome-repositories.com/repository/kootenpv-whereami.md

## Topics

`access-point` `cross-platform` `distance` `hacktoberfest` `hacktoberfest2021` `indoor-positioning` `whereami` `wifi-signal`

## Description

whereami is an indoor geolocation tool and WiFi indoor positioning system designed to estimate a user's physical position within a building. It functions as a machine learning location predictor and WiFi signal scanner that maps wireless network patterns to specific geographic coordinates or room labels.

The system utilizes a predictive pipeline to train models that translate wireless network fingerprints into location data. It identifies physical positions by analyzing signal strengths from available wireless infrastructure and comparing live scans against trained databases of known signal fingerprints.

The project covers cross-platform network scanning to collect structured WiFi signal data across different operating systems. It also includes capabilities for WiFi signal mapping and the development of supervised learning pipelines to associate labeled signal datasets with geographic ground truth.

## Tags

### Artificial Intelligence & ML

- [Indoor Positioning Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/indoor-positioning-systems.md) — Implements a predictive pipeline that estimates physical position by comparing live signal scans against trained fingerprints.
- [Localization Model Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/localization-model-training.md) — Trains predictive models to translate wireless network fingerprints into specific geographic coordinates or room labels.
- [Supervised Learning Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/supervised-learning-pipelines.md) — Provides a sequential workflow for training predictive models by associating signal datasets with geographic ground truth.

### Part of an Awesome List

- [Indoor Geolocation](https://awesome-repositories.com/f/awesome-lists/data/geographic-and-location/indoor-geolocation.md) — Estimates a user's geographic position inside a building by analyzing wireless signal strengths through ML models. ([source](https://github.com/kootenpv/whereami#readme))

### Development Tools & Productivity

- [Location Predictors](https://awesome-repositories.com/f/development-tools-productivity/task-pipeline-managers/machine-learning-pipelines/location-predictors.md) — Provides a predictive pipeline that trains models to map wireless network patterns to specific geographic areas.

### Hardware & IoT

- [Indoor Geolocation Tools](https://awesome-repositories.com/f/hardware-iot/indoor-geolocation-tools.md) — Provides a complete system for estimating a user's position within a building using wireless infrastructure.
- [Signal Fingerprinting](https://awesome-repositories.com/f/hardware-iot/radio-frequency-signal-processing/signal-visualizers/wifi-signal-mapping/signal-fingerprinting.md) — Maps unique wireless signal intensity patterns to specific physical coordinates to determine current location.
- [WiFi Positioning Systems](https://awesome-repositories.com/f/hardware-iot/radio-frequency-signal-processing/signal-visualizers/wifi-signal-mapping/wifi-positioning-systems.md) — Implements a location estimation tool using WiFi signal strength and machine learning to predict physical coordinates.
- [WiFi Signal Mapping](https://awesome-repositories.com/f/hardware-iot/radio-frequency-signal-processing/signal-visualizers/wifi-signal-mapping.md) — Scans and records wireless access point data to create a digital map of signal strengths across a physical area.

### Networking & Communication

- [Wireless Network Scanning](https://awesome-repositories.com/f/networking-communication/wireless-network-scanning.md) — Provides a cross-platform mechanism to scan and list nearby wireless access points and their signal parameters.
- [Cross-Platform Signal Collection](https://awesome-repositories.com/f/networking-communication/cross-platform-networking/cross-platform-signal-collection.md) — Collects structured WiFi signal data from different operating systems to feed into location estimation pipelines.

### System Administration & Monitoring

- [Indoor Location Tracking](https://awesome-repositories.com/f/system-administration-monitoring/real-time-monitoring/indoor-location-tracking.md) — Estimates a user's physical position inside a building by analyzing WiFi signal strengths and machine learning patterns.