Active Actions in the Extraction of Urban Objects for Information Quality and Knowledge Recommendation with Machine Learning

Luis Augusto Silva; André Sales Mendes; Héctor Sánchez San Blas; Lia Caetano Bastos; Alexandre Leopoldo Gonçalves; André Fabiano de Moraes

doi:10.3390/s23010138

Active Actions in the Extraction of Urban Objects for Information Quality and Knowledge Recommendation with Machine Learning

Sensors (Basel). 2022 Dec 23;23(1):138. doi: 10.3390/s23010138.

Authors

Luis Augusto Silva¹, André Sales Mendes¹, Héctor Sánchez San Blas¹, Lia Caetano Bastos², Alexandre Leopoldo Gonçalves², André Fabiano de Moraes^{2

3}

Affiliations

¹ Expert Systems and Applications Lab (ESALAB), Faculty of Science, University of Salamanca, 37008 Salamanca, Spain.
² Department of Knowledge Engineering and Management, Federal University of Santa Catarina, Florianopolis 88040-900, Brazil.
³ Department Information Technology, IT Institute Federal of Science Technology IFC, Camboriú 88340-055, Brazil.

Abstract

Due to the increasing urban development, it has become important for municipalities to permanently understand land use and ecological processes, and make cities smart and sustainable by implementing technological tools for land monitoring. An important problem is the absence of technologies that certify the quality of information for the creation of strategies. In this context, expressive volumes of data are used, requiring great effort to understand their structures, and then access information with the desired quality. This study are designed to provide an initial response to the need for mapping zones in the city of Itajaí (SC), Brazil. The solution proposes to aid object recognition employing object-based classifiers OneR, NaiveBayes, J48, IBk, and Hoeffding Tree algorithms used together with GeoDMA, and a first approach in the use of Region-based Convolutional Neural Network (R-CNN) and the YOLO algorithm. All this is to characterize vegetation zones, exposed soil zones, asphalt, and buildings within an urban and rural area. Through the implemented model for active identification of geospatial objects with similarity levels, it was possible to apply the data crossover after detecting the best classifier with accuracy (85%) and the kappa agreement coefficient (76%). The case study presents the dynamics of urban and rural expansion, where expressive volumes of data are obtained and submitted to different methods of cataloging and preparation to subsidize rapid control actions. Finally, the research describes a practical and systematic approach, evaluating the extraction of information to the recommendation of knowledge with greater scientific relevance. Allowing the methods presented to apply the calibration of values for each object, to achieve results with greater accuracy, which is proposed to help improve conservation and management decisions related to the zones within the city, leaving as a legacy the construction of a minimum technological infrastructure to support the decision.

Keywords: gis detection; information extraction; machine learning; object spatial; smart cities.

MeSH terms

Brazil
Cities
Machine Learning*
Neural Networks, Computer*

Grants and funding

The research of Luis Augusto Silva has been funded by the call for predoctoral contracts USAL 2021, co-financed by Banco Santander. Héctor Sánchez San Blas’s research was supported by the Spanish Ministry of Universities (FPU Fellowship under Grant FPU20/03014).