Laboratory-based spectral data acquisition of roof materials

Zambrano-Prado, Perla; Josa, Alejandro; Rieradevall, Joan; Pérez-Aragüés, Fernando; Marchan, Juan Fernando; Gassó-Domingo, Santiago; Gabarrell, Xavier

doi:10.1080/01431161.2020.1798548

Cited by 3 publications

(1 citation statement)

References 47 publications

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“…The London Urban Micromet data-Archive Spectral Library of impervious Urban Materials (LUMA SLUM) contains 16 roof material spectra acquired over London, UK, in the spectral range 350-2500 nm [29]. Zambrano-Prado et al [30] built a 39 roof-material spectral library, representative of the Mediterranean regions, in the spectral range 400-1000 nm. These spectral features were acquired either in situ, in a laboratory, or via a combination of airborne and in situ data.…”

Section: Discussionmentioning

confidence: 99%

WaRM: A Roof Material Spectral Library for Wallonia, Belgium

Wyard

Marion

Hallot

2023

Data

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The exploitation of urban-material spectral properties is of increasing importance for a broad range of applications, such as urban climate-change modeling and mitigation or specific/dangerous roof-material detection and inventory. A new spectral library dedicated to the detection of roof material was created to reflect the regional diversity of materials employed in Wallonia, Belgium. The Walloon Roof Material (WaRM) spectral library accounts for 26 roof material spectra in the spectral range 350–2500 nm. Spectra were acquired using an ASD FieldSpec3 Hi-Res spectrometer in laboratory conditions, using a spectral sampling interval of 1 nm. The analysis of the spectra shows that spectral signatures are strongly influenced by the color of the roof materials, at least in the VIS spectral range. The SWIR spectral range is in general more relevant to distinguishing the different types of material. Exceptions are the similar properties and very close spectra of several black materials, meaning that their spectral signatures are not sufficiently different to distinguish them from each other. Although building materials can vary regionally due to different available construction materials, the WaRM spectral library can certainly be used for wider applications; Wallonia has always been strongly connected to the surrounding regions and has always encountered climatic conditions similar to all of Northwest Europe.

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Section: Discussionmentioning

confidence: 99%

WaRM: A Roof Material Spectral Library for Wallonia, Belgium

Wyard

Marion

Hallot

2023

Data

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Urban hyperspectral reference data availability and reuse: State‐of‐the‐practice review

Salcido,

Laefer

2024

The Photogrammetric Record

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Hyperspectral remote sensing is currently underutilized in urban environments due to significant barriers concerning the existence, availability, and quality of urban hyperspectral reference spectra. This paper exposes these barriers by identifying, cataloging, and characterizing the contents of 23 spectral libraries, developing metrics to assess compliance with the Principles of Findability, Accessibility, Interoperability, and Reusability (FAIR), and evaluating existing resources using these criteria. Only 2931 urban spectral records were found within the 4 Global Spectral Libraries (0.61% of 476,592 published spectra). Within a further 19 Local Urban Spectral Libraries, 3862 additional urban spectra were found, but only 1662 (43%) were accessible without restriction. Content analysis revealed insufficient representation of urban material heterogeneity, imbalanced categories, and limited library interoperability, all of which further hinder effective data utilization. In response, this paper proposes a 14‐category metadataset, with specific considerations to overcome environmentally induced and inherent, intra‐material variability. In addition, material‐based spectral groupings and data resampling to common hyperspectral equipment specifications are recommended. These measures aim to enhance the utility of urban spectral libraries by improving FAIR compliance, thereby contributing to a more cohesive and enduring framework for hyperspectral reference data.

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Machine Learning-Based Handling of Geospatial Big Data From Hyperspectral Sensors for Urban Area Characterization

Mishra

Garg

2023

Advances in Geospatial Technologies

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Investigating urban processes requires detailed built-up surface material composition information, which is possible through constant monitoring by recently launched spaceborne hyperspectral sensors. However, they are plagued by medium spatial resolutions and mixed pixels. Super-resolution (SR) and spectral unmixing can address the former and latter, respectively. Machine learning, due to its predictive capability, has become indispensable for studying these datasets. Hence, remote sensing researchers, need to understand machine learning algorithms thoroughly. Herein, works on machine learning application for urban hyperspectral sensing have been thoroughly analyzed. A functional SR classification scheme has also been introduced. Super-resolved product quality metric evaluation, open-source urban spectral libraries, benchmark urban scenes meant for training and testing new SR, or unmixing algorithms have been briefly reviewed. Finally, difficulties with hyperspectral image processing based on machine learning have been raised, along with future research directions.

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Laboratory-based spectral data acquisition of roof materials

Cited by 3 publications

References 47 publications

WaRM: A Roof Material Spectral Library for Wallonia, Belgium

WaRM: A Roof Material Spectral Library for Wallonia, Belgium

Urban hyperspectral reference data availability and reuse: State‐of‐the‐practice review

Machine Learning-Based Handling of Geospatial Big Data From Hyperspectral Sensors for Urban Area Characterization

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