A MACHINE LEARNING PIPELINE ARTICULATING SATELLITE IMAGERY AND OPENSTREETMAP FOR ROAD DETECTION
<p>Satellite imagery from earth observation missions enable processing big data to gather information about the world. Automatizing the creation of maps that reflect ground truth is a desirable outcome that would aid decision makers to take adequate actions in alignment with the United Nations...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2019-08-01
|
Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W14/255/2019/isprs-archives-XLII-4-W14-255-2019.pdf |
id |
doaj-35dfaa288a3c428d8f0db7af21954f81 |
---|---|
record_format |
Article |
spelling |
doaj-35dfaa288a3c428d8f0db7af21954f812020-11-24T22:15:15ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342019-08-01XLII-4-W1425526010.5194/isprs-archives-XLII-4-W14-255-2019A MACHINE LEARNING PIPELINE ARTICULATING SATELLITE IMAGERY AND OPENSTREETMAP FOR ROAD DETECTIONM. A. Zurbaran0P. Wightman1M. A. Brovelli2Dept. of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milano MI, ItalyDept. of Systems Engineering, Universidad del Norte, km 5 via Pto. Colombia, Atlántico, ColombiaDept. of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milano MI, Italy<p>Satellite imagery from earth observation missions enable processing big data to gather information about the world. Automatizing the creation of maps that reflect ground truth is a desirable outcome that would aid decision makers to take adequate actions in alignment with the United Nations Sustainable Development Goals. In order to harness the power that the availability of the new generation of satellites enable, it is necessary to implement techniques capable of handling annotations for the massive volume and variability of high spatial resolution imagery for further processing. However, the availability of public datasets for training machine learning models for image segmentation plays an important role for scalability.</p><p>This work focuses on bridging remote sensing and computer vision by providing an open source based pipeline for generating machine learning training datasets for road detection in an area of interest. The proposed pipeline addresses road detection as a binary classification problem using road annotations existing in OpenStreetMap for creating masks. For this case study, Planet images of 3m resolution are used for creating a training dataset for road detection in Kenya.</p>https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W14/255/2019/isprs-archives-XLII-4-W14-255-2019.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
M. A. Zurbaran P. Wightman M. A. Brovelli |
spellingShingle |
M. A. Zurbaran P. Wightman M. A. Brovelli A MACHINE LEARNING PIPELINE ARTICULATING SATELLITE IMAGERY AND OPENSTREETMAP FOR ROAD DETECTION The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
author_facet |
M. A. Zurbaran P. Wightman M. A. Brovelli |
author_sort |
M. A. Zurbaran |
title |
A MACHINE LEARNING PIPELINE ARTICULATING SATELLITE IMAGERY AND OPENSTREETMAP FOR ROAD DETECTION |
title_short |
A MACHINE LEARNING PIPELINE ARTICULATING SATELLITE IMAGERY AND OPENSTREETMAP FOR ROAD DETECTION |
title_full |
A MACHINE LEARNING PIPELINE ARTICULATING SATELLITE IMAGERY AND OPENSTREETMAP FOR ROAD DETECTION |
title_fullStr |
A MACHINE LEARNING PIPELINE ARTICULATING SATELLITE IMAGERY AND OPENSTREETMAP FOR ROAD DETECTION |
title_full_unstemmed |
A MACHINE LEARNING PIPELINE ARTICULATING SATELLITE IMAGERY AND OPENSTREETMAP FOR ROAD DETECTION |
title_sort |
machine learning pipeline articulating satellite imagery and openstreetmap for road detection |
publisher |
Copernicus Publications |
series |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
issn |
1682-1750 2194-9034 |
publishDate |
2019-08-01 |
description |
<p>Satellite imagery from earth observation missions enable processing big data to gather information about the world. Automatizing the creation of maps that reflect ground truth is a desirable outcome that would aid decision makers to take adequate actions in alignment with the United Nations Sustainable Development Goals. In order to harness the power that the availability of the new generation of satellites enable, it is necessary to implement techniques capable of handling annotations for the massive volume and variability of high spatial resolution imagery for further processing. However, the availability of public datasets for training machine learning models for image segmentation plays an important role for scalability.</p><p>This work focuses on bridging remote sensing and computer vision by providing an open source based pipeline for generating machine learning training datasets for road detection in an area of interest. The proposed pipeline addresses road detection as a binary classification problem using road annotations existing in OpenStreetMap for creating masks. For this case study, Planet images of 3m resolution are used for creating a training dataset for road detection in Kenya.</p> |
url |
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W14/255/2019/isprs-archives-XLII-4-W14-255-2019.pdf |
work_keys_str_mv |
AT mazurbaran amachinelearningpipelinearticulatingsatelliteimageryandopenstreetmapforroaddetection AT pwightman amachinelearningpipelinearticulatingsatelliteimageryandopenstreetmapforroaddetection AT mabrovelli amachinelearningpipelinearticulatingsatelliteimageryandopenstreetmapforroaddetection AT mazurbaran machinelearningpipelinearticulatingsatelliteimageryandopenstreetmapforroaddetection AT pwightman machinelearningpipelinearticulatingsatelliteimageryandopenstreetmapforroaddetection AT mabrovelli machinelearningpipelinearticulatingsatelliteimageryandopenstreetmapforroaddetection |
_version_ |
1725795195198373888 |