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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 main features of kootenpv/whereami are: Indoor Positioning Systems, Localization Model Training, Supervised Learning Pipelines, Indoor Geolocation, Location Predictors, Indoor Geolocation Tools, Signal Fingerprinting, WiFi Positioning Systems.
Projects with overlapping indexed features include: schollz/find — This project is an indoor location tracking server and wireless fingerprinting engine designed to calculate the… schollz/find3 — find3 is an indoor positioning framework that determines physical locations inside buildings by analyzing wireless… vremsoftwaredevelopment/wifianalyzer — WiFiAnalyzer is an IEEE 802.11 network analyzer and signal monitor used to scan wireless access points, monitor signal… commaai/research — This research framework provides a deep learning driving simulator and a multimodal data pipeline for autonomous… chrisk44/hijacker — Hijacker is a Wi-Fi security auditing suite designed for scanning wireless networks, capturing traffic, and recovering… arismelachroinos/lscript — lscript is a wireless network pentesting framework and keyboard-driven command console. It functions as a security…
This project is an indoor location tracking server and wireless fingerprinting engine designed to calculate the physical position of Wi-Fi enabled devices within a building. It functions as a local positioning framework that maps wireless signal strengths to physical space without the need for dedicated hardware beacons. The system utilizes a training process to collect and submit wireless signal data, establishing a baseline database of signal patterns for specific coordinates. It identifies current locations by matching real-time signals against these stored fingerprints using k-nearest nei
find3 is an indoor positioning framework that determines physical locations inside buildings by analyzing wireless signal patterns and hardware fingerprints. It functions as a machine learning location classifier that matches real-time signal data against a database of known fingerprints to identify internal positions. The system includes a wireless signal fingerprinting tool used to scan hardware interfaces and collect signal strength data for location mapping. This data is maintained in a compressed relational signal database designed for fast retrieval of hardware addresses and signal stre
WiFiAnalyzer is an IEEE 802.11 network analyzer and signal monitor used to scan wireless access points, monitor signal strength, and identify network specifications. It functions as a wireless hardware diagnostic tool and site survey system for collecting and exporting detailed wireless network metadata. The project distinguishes itself through capabilities for calculating approximate physical proximity to networks using received signal strength indicators and mapping signal levels across frequencies to identify channel congestion. It also includes a wireless spectrum visualizer for graphing
Hijacker is a Wi-Fi security auditing suite designed for scanning wireless networks, capturing traffic, and recovering credentials. It provides a set of tools for detecting nearby access points and clients, intercepting WPA handshakes, and recovering WPA and WEP passwords. The project features a visual security audit interface that allows for the execution of specialized tools without using a command-line terminal. It includes a dedicated WPS pin recovery tool for extracting access point pins using pixie-dust attacks via external adapters. The toolkit covers network reconnaissance, including