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Your search keyword '"Very high resolution mapping"' showing total 4 results
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4 results on '"Very high resolution mapping"'

1. Deploying machine learning to assist digital humanitarians: making image annotation in OpenStreetMap more efficient.

Author

Vargas Muñoz, John E., Tuia, Devis, and Falcão, Alexandre X.

Subjects
*MACHINE learning, *HUMAN-computer interaction, *RURAL population, *VOLUNTEER service, *ANNOTATIONS, *CONVOLUTIONAL neural networks
Abstract

Locating populations in rural areas of developing countries has attracted the attention of humanitarian mapping projects since it is important to plan actions that affect vulnerable areas. Recent efforts have tackled this problem as the detection of buildings in aerial images. However, the quality and the amount of rural building annotated data in open mapping services like OpenStreetMap (OSM) is not sufficient for training accurate models for such detection. Although these methods have the potential of aiding in the update of rural building information, they are not accurate enough to automatically update the rural building maps. In this paper, we explore a human-computer interaction approach and propose an interactive method to support and optimize the work of volunteers in OSM. The user is asked to verify/correct the annotation of selected tiles during several iterations and therefore improving the model with the new annotated data. The experimental results, with simulated and real user annotation corrections, show that the proposed method greatly reduces the amount of data that the volunteers of OSM need to verify/correct. The proposed methodology could benefit humanitarian mapping projects, not only by making more efficient the process of annotation but also by improving the engagement of volunteers. [ABSTRACT FROM AUTHOR]

Published
2021
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2. Correcting rural building annotations in OpenStreetMap using convolutional neural networks.

Author

Vargas-Muñoz, John E., Lobry, Sylvain, Falcão, Alexandre X., and Tuia, Devis

Subjects
*BUILDINGS, *PROBABILITY theory, *MARKOV random fields, *STATISTICAL correlation, *ARTIFICIAL neural networks
Abstract

Abstract 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. [ABSTRACT FROM AUTHOR]

Published
2019
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3. Correcting rural building annotations in OpenStreetMap using convolutional neural networks

Author

Alexandre X. Falcão, Sylvain Lobry, John E. Vargas-Munoz, and Devis Tuia

Subjects
FOS: Computer and information sciences, 010504 meteorology & atmospheric sciences, Computer science, Computer Vision and Pattern Recognition (cs.CV), 0211 other engineering and technologies, Computer Science - Computer Vision and Pattern Recognition, 02 engineering and technology, Plan (drawing), computer.software_genre, 01 natural sciences, Convolutional neural network, Image (mathematics), Annotation, Laboratory of Geo-information Science and Remote Sensing, Laboratorium voor Geo-informatiekunde en Remote Sensing, Computers in Earth Sciences, Engineering (miscellaneous), 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Markov random field, Artificial neural network, business.industry, OpenStreetMap, PE&RC, Very high resolution mapping, Atomic and Molecular Physics, and Optics, Computer Science Applications, Update of vector maps, Convolutional neural networks, Artificial intelligence, Data mining, business, computer, Shape priors, Volunteered geographical information
Abstract

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.

Published
2019

4. Correcting rural building annotations in OpenStreetMap using convolutional neural networks

Author

Vargas Muñoz, John Edgar, 1991, Falcão, Alexandre Xavier, 1966, and UNIVERSIDADE ESTADUAL DE CAMPINAS

Subjects
Mapas vetoriais, Artigo original, Convolutional neural networks, OpenStreetMap, Redes neurais convolucionais, Very high resolution mapping, Shape priors, Volunteered geographical information, Vector maps
Abstract

Agradecimentos: This research was funded by FAPESP (Grant 2016/14760-5, 2017/10086-0 and 2014/12236-1), the CNPq (Grant 302970/2014-2) and by the Swiss National Science Foundation (Grant PP00P2-150593) Abstract: 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 CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP Fechado

Published
2019

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