Correcting rural building annotations in OpenStreetMap using convolutional neural networks

Autor(es)

Vargas-Munoz, John E. ^[1] ; Lobry, Sylvain ^[2] ; Falcao, Alexandre X. ^[1] ; Tuia, Devis ^[2]

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, Inst Comp, Lab Image Data Sci, Campinas, SP - Brazil

^[2] Wageningen Univ & Res, Lab Geoinformat Sci & Remote Sensing, Wageningen - Netherlands

• Tipo de documento: Artigo Científico
• Fonte: ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING; v. 147, p. 283-293, JAN 2019.
• Citações Web of Science: 2

Resumo

Rural building mapping is paramount to support demographic studies and plan actions in response to crisis that affect those areas. Rural building annotations exist in OpenStreetMap (OSM), but their quality and quantity are not sufficient for training models that can create accurate rural building maps. The problems with these annotations essentially fall into three categories: (i) most commonly, many annotations are geometrically misaligned with the updated imagery; (ii) some annotations do not correspond to buildings in the images (they are misannotations or the buildings have been destroyed); and (iii) some annotations are missing for buildings in the images (the buildings were never annotated or were built between subsequent image acquisitions). First, we propose a method based on Markov Random Field (MRF) to align the buildings with their annotations. The method maximizes the correlation between annotations and a building probability map while enforcing that nearby buildings have similar alignment vectors. Second, the annotations with no evidence in the building probability map are removed. Third, we present a method to detect non-annotated buildings with predefined shapes and add their annotation. The proposed methodology shows considerable improvement in accuracy of the OSM annotations for two regions of Tanzania and Zimbabwe, being more accurate than state-of-the-art baselines. (AU)

• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 16/14760-5 - Anotação Interativa de Imagens de Sensoriamento Remoto
• Beneficiário: John Edgar Vargas Muñoz
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 17/10086-0 - Detecção e delineamento de prédios rurais utilizando imagens de sensoriamento remoto
• Beneficiário: John Edgar Vargas Muñoz
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Doutorado