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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

U-Net-Id, an Instance Segmentation Model for Building Extraction from Satellite Images-Case Study in the Joanopolis City, Brazil

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Autor(es):
Wagner, Fabien H. [1, 2] ; Dalagnol, Ricardo [1] ; Tarabalka, Yuliya [3, 4] ; Segantine, Tassiana Y. F. [2] ; Thome, Rogerio [2] ; Hirye, Mayumi C. M. [5]
Número total de Autores: 6
Afiliação do(s) autor(es):
[1] Natl Inst Space Res INPE, Remote Sensing Div, BR-12227010 Sao Jose Dos Campos, SP - Brazil
[2] Fdn Sci Technol & Space Applicat FUNCATE, GeoProc Div, BR-12210131 Sao Jose Dos Campos, SP - Brazil
[3] INRIA Sophia Antipolis, F-06902 Sophia Antipolis - France
[4] Luxcarta Technol, Parc Activite Argile, Lot 119b, F-06370 Mouans Sartoux - France
[5] Univ Sao Paulo, Fac Architecture & Urbanism, Quapa Lab, BR-05508900 Sao Paulo, SP - Brazil
Número total de Afiliações: 5
Tipo de documento: Artigo Científico
Fonte: REMOTE SENSING; v. 12, n. 10 MAY 2020.
Citações Web of Science: 1
Resumo

Currently, there exists a growing demand for individual building mapping in regions of rapid urban growth in less-developed countries. Most existing methods can segment buildings but cannot discriminate adjacent buildings. Here, we present a new convolutional neural network architecture (CNN) called U-net-id that performs building instance segmentation. The proposed network is trained with WorldView-3 satellite RGB images (0.3 m) and three different labeled masks. The first is the building mask; the second is the border mask, which is the border of the building segment with 4 pixels added outside and 3 pixels inside; and the third is the inner segment mask, which is the segment of the building diminished by 2 pixels. The architecture consists of three parallel paths, one for each mask, all starting with a U-net model. To accurately capture the overlap between the masks, all activation layers of the U-nets are copied and concatenated on each path and sent to two additional convolutional layers before the output activation layers. The method was tested with a dataset of 7563 manually delineated individual buildings of the city of Joanopolis-SP, Brazil. On this dataset, the semantic segmentation showed an overall accuracy of 97.67% and an F1-Score of 0.937 and the building individual instance segmentation showed good performance with a mean intersection over union (IoU) of 0.582 (median IoU = 0.694). (AU)

Processo FAPESP: 15/22987-7 - Avaliação do impacto de mudanças climáticas sobre a dinâmica de biomassa e carbono na Amazônia
Beneficiário:Ricardo Dal'Agnol da Silva
Linha de fomento: Bolsas no Brasil - Doutorado
Processo FAPESP: 16/17652-9 - Diversidade funcional dos biomas Amazônia, Mata Atlântica e Cerrado nos ambientes intactos e em regeneração por meio de imagens hiperspectrais
Beneficiário:Fabien Hubert Wagner
Linha de fomento: Bolsas no Brasil - Apoio a Jovens Pesquisadores
Processo FAPESP: 15/50484-0 - Diversidade funcional dos biomas Amazônia, Mata Atlântica e Cerrado nos ambientes intactos e em regeneração por meio de imagens hiperspectrais
Beneficiário:Fabien Hubert Wagner
Linha de fomento: Auxílio à Pesquisa - Apoio a Jovens Pesquisadores