Detecting shadows in a segmented Land Use Land Cover image with LIDAR data

Agirre, Ainhoa; Malpica, José A.

doi:10.1109/igarss.2012.6352371

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Lidar1

Shadow Detection and Correction Of Dark Regions Labeled As W1

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Cited by 2 publications

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“…These researchers found that the combination of these two types of data allowed extraction of land uses within shadowed areas. In a second study, researchers successfully used aerial photos and Lidar to identify land use in shadows within an urban areathe City of Alcalá, Madrid, Spain [58].…”

Section: Lidarmentioning

confidence: 99%

Land Use/Land Cover Monitoring and Geospatial Technologies: An Overview

Parece

Campbell

2015

The Handbook of Environmental Chemistry

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Section: Lidarmentioning

confidence: 99%

Land Use/Land Cover Monitoring and Geospatial Technologies: An Overview

Parece

Campbell

2015

The Handbook of Environmental Chemistry

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“…This is achieved by generating a shadow image from the flight data from which the image was taken. The shadow image is generated using an algorithm developed by our research group [49]. By combining this image and the RAG, it is possible to determine the regions that were in a shadowed area and were labeled as sheets of water.…”

Section: Shadow Detection and Correction Of Dark Regions Labeled As Wmentioning

confidence: 99%

Semi-Automatic Detection of Swimming Pools from Aerial High-Resolution Images and LIDAR Data

Rodríguez-Cuenca

Alonso

2014

Remote Sensing

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Bodies of water, particularly swimming pools, are land covers of high interest. Their maintenance involves energy costs that authorities must take into consideration. In addition, swimming pools are important water sources for firefighting. However, they also provide a habitat for mosquitoes to breed, potentially posing a serious health threat of mosquito-borne disease. This paper presents a novel semi-automatic method of detecting swimming pools in urban environments from aerial images and LIDAR data. A new index for detecting swimming pools is presented (Normalized Difference Swimming Pools Index) that is combined with three other decision indices using the Dempster-Shafer theory to determine the locations of swimming pools. The proposed method was tested in an urban area of the city of Alcalá de Henares in Madrid, Spain. The method detected all existing swimming pools in the studied area with an overall accuracy of 99.86%, similar to the results obtained by support vector machines (SVM) supervised classification.

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Detecting shadows in a segmented Land Use Land Cover image with LIDAR data

Cited by 2 publications

References 5 publications

Land Use/Land Cover Monitoring and Geospatial Technologies: An Overview

Land Use/Land Cover Monitoring and Geospatial Technologies: An Overview

Semi-Automatic Detection of Swimming Pools from Aerial High-Resolution Images and LIDAR Data

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