A Prototype Machine Learning Tool Aiming to Support 3D Crowdsourced Cadastral Surveying of Self-Made Cities

Abstract

Land administration and management systems (LAMSs) have already made progress in the field of 3D Cadastre and the visualization of complex urban properties to support property markets and provide geospatial information for the sustainable management of smart cities. However, in less developed economies, with informally developed urban areas—the so-called self-made cities—the 2D LAMSs are left behind. Usually, they are less effective and mainly incomplete since a large number of informal constructions remain unregistered. This paper presents the latest results of an innovative on-going research aiming to structure, test and propose a low-cost but reliable enough methodology to support the simultaneous and fast implementation of both 2D land parcel and 3D property unit registration of informal, multi-story and unregistered constructions. An Indoor Positioning System (IPS) built upon low-cost Bluetooth technology combined with an innovative machine learning algorithm and connected with a 3D LADM-based cadastral mapping mobile application are the two key components of the technical solution under investigation. The proposed solution is tested for the first floor of a multi-room office building. The main conclusions concern the potential, usability and reliability of the method.

Keywords

• 3D Cadastre
• crowdsourcing
• 3D mapping
• machine learning
• indoor localization
• informal development