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01-03: Ways to establish cadastral systems at scale
8:30am - 10:00am
Session Chair: Claire Galpin, World Bank, United States of America
ID: 806 / 01-03: 1 Individual Papers Topics: New ways of land data capture & analysis (incl. machine learning) Keywords: Geomatics, Data Integration, Data Interoperability, Geospatial, Cadastre, Survey
Increasing cadastral survey productivity to tackle undocumented land rights worldwide: A case study
Trimble Inc, United States of America
This presentation will introduce a vision for transforming cadastral workflows by leveraging a broad spectrum of geospatial technologies in a way that will provide surveyors with greater productivity in both the field and the office. A holistic system approach will be analyzed, with key factors identified to address customer challenges in the context of a real-world case study. Finally, the customer benefits identified in the case study will be extrapolated to identify potential applicability to developing countries in order to enhance productivity to tackle undocumented land rights worldwide.
ID: 396 / 01-03: 2 Individual Papers Topics: New ways of land data capture & analysis (incl. machine learning) Keywords: Mapping, Lidar, images, planning
Leica Geosystem, Denmark
The world's population is increasing fast; people are moving from country to cities, and the environment is changing quickly. For development projects, basic Mapping has for years been a need. A major problem has been the time it took from the start of a mapping project until data were available for the real development project and sometimes the quality of data. This paper is telling how technology from the new continental mapping project there deliver maps to the Internet portals map solutions can be used in development projects in e.g. Africa. By exampels of already done projects and describing the new methodes there are shown examples of how large scale mapping can be done from aircrafts with significant more accurate data there can compete both in price an performance with satellite data.
ID: 698 / 01-03: 3 Individual Papers Topics: New ways of land data capture & analysis (incl. machine learning) Keywords: Land administration; land registration; land use planning; digital land data capture; decentralized and self-sustaining.
An innovative affordable and decentralized model for land registration and administration at a national scale in Tanzania
Tressan Sullivan, Malaki Msigwa, Gilead Mushaija, Mustapha Issa, Alexander Sololov
DAI Global LLC, Tanzania
This paper addresses issues related to scaling up a successful, innovative land registration pilot program using digital technology. Following the successful development of a process for a decentralised land administration system—driven by local land administration authorities using digital land data capture and management tools in Tanzania—this paper explores the potential for and challenges of implementing the system nationally. The paper proposes a low-cost, participatory, digital land use planning, registration, and management process. It examines the potential for a self-sustaining, decentralized, digital land management system for large-scale first land registration and ongoing administration of post-registration transactions. It is proposed that contributions by beneficiaries in conjunction with the involvement of the private and nongovernment organization (NGO) sectors can potentially deliver a self-sustaining system. The paper further examines challenges related to secure data storage and limiting opportunities for corruption.
ID: 356 / 01-03: 4 Individual Papers Topics: New ways of land data capture & analysis (incl. machine learning) Keywords: machine learning, valuation
Leveraging location-enabled street photos and machine learning to automate large-scale data collection in support of property valuation
Tim Fella, Katherine Smyth
ESRI, United States of America
To address the data divide for property valuation, a proof of concept is proposed that leverages Esri’s Property Condition Survey together with artificial intelligence. The Property Condition Survey is a configuration of Esri’s Photo Survey application that can be used by local governments to publish street-level photo collections, conduct property surveys, and automate the classification of property condition using machine learning.
The Property Condition Survey leverages location-enabled photos produced by many commercially available cameras and simplifies data processing, so street-level photo collections can be gathered on a regular basis. Photo collections can then be used in the Property Condition Survey application and/or be classified using Microsoft's Custom Vision service to identify property conditions and related attributes in support of property valuation.
By applying machine learning (ML) to the classification of street-level property photos, valuation authorities can significantly reduce the time and cost associated with performing property assessments in the field.