Crowd-sourced data collection to support automatic classification of building footprint data

Robert Hecht,Matthias Kalla,Tobias Krüger

doi:10.5194/ica-proc-1-54-2018

Abstract

Abstract. Human settlements are mainly formed by buildings with their different characteristics and usage. Despite the importance of buildings for the economy and society, complete regional or even national figures of the entire building stock and its spatial distribution are still hardly available. Available digital topographic data sets created by National Mapping Agencies or mapped voluntarily through a crowd via Volunteered Geographic Information (VGI) platforms (e.g. OpenStreetMap) contain building footprint information but often lack additional information on building type, usage, age or number of floors. For this reason, predictive modeling is becoming increasingly important in this context. The capabilities of machine learning allow for the prediction of building types and other building characteristics and thus, the efficient classification and description of the entire building stock of cities and regions. However, such data-driven approaches always require a sufficient amount of ground truth (reference) information for training and validation. The collection of reference data is usually cost-intensive and time-consuming. Experiences from other disciplines have shown that crowdsourcing offers the possibility to support the process of obtaining ground truth data. Therefore, this paper presents the results of an experimental study aiming at assessing the accuracy of non-expert annotations on street view images collected from an internet crowd. The findings provide the basis for a future integration of a crowdsourcing component into the process of land use mapping, particularly the automatic building classification.

Highlights

Digital building models from National Mapping and Cadastral Agencies (NMCA) or Volunteered Geographic Information (VGI) platforms often lack attribute information, such as the building usage, housing type, number of floors, building height, and years of construction
We want to explore the potential of crowdsourcing in the context of mapping and monitoring urban land use, the classification of building footprints in digital topographic databases
In the last few years, a number of different terms from different disciplines have emerged that describe the process of citizen-based sensing of geographic information, namely crowdsourcing, citizen science, collaborative mapping or the crowd-sourced information itself, such as Volunteered Geographic Information (VGI) or User-Generated Content (UGC)

Summary

Introduction

Digital building models from National Mapping and Cadastral Agencies (NMCA) or Volunteered Geographic Information (VGI) platforms often lack attribute information, such as the building usage, housing type, number of floors, building height, and years of construction This information is of particular importance for various research domains and applications such as spatial science, geography, urban planning, architecture, and disaster management. Supervised machine learning techniques help to classify the building footprints according to a predefined building typology and to semantically enrich the datasets with additional information Such data-driven approaches provide promising results with high accuracies for single cities and regions Changing the spatial and cultural context (e.g. other regions, countries, continents etc.) requires the collection of additional ground truth data in the specific area under investigation for model training and validating. We prefer using the term crowdsourcing defined as a type of participative online activity, the process of a voluntary undertaking of specific tasks

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Conclusion