WaRM: A Roof Material Spectral Library for Wallonia, Belgium
Abstract
:1. Summary
2. Methods
2.1. Sampling
2.2. Measurement Protocol
2.3. Final Spectral Signature Processing
3. Data Description
3.1. Tiles
3.2. Metals
3.3. Slates and Corrugated Sheets
3.4. Membranes, PVC and Solar Panels
4. Conclusions and Prospects
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Hamdi, R.; Kusaka, H.; Van Doan, Q.; Cai, P.; He, H.; Luo, G.; Kuang, W.; Caluwaerts, S.; Duchêne, F.; Van Schaeybroek, B.; et al. The State-of-the-Art of Urban Climate Change Modeling and Observations. Earth Syst. Environ. 2020, 4, 631–646. [Google Scholar] [CrossRef]
- Al Qattan, A.A.M. Using Cool Coating for Pavements, Asphalt, Façades and Building Roofs in the Urban Environment to Reduce the Summer Urban Heat Effect in Giza Square, Egypt. In Innovating Strategies and Solutions for Urban Performance and Regeneration; Piselli, C., Altan, H., Balaban, O., Kremer, P., Eds.; Springer International Publishing: Cham, Switzerland, 2022; pp. 117–126. ISBN 978-3-030-98187-7. [Google Scholar]
- Santamouris, M.; Yun, G.Y. Recent Development and Research Priorities on Cool and Super Cool Materials to Mitigate Urban Heat Island. Renew. Energy 2020, 161, 792–807. [Google Scholar] [CrossRef]
- Abriha, D.; Kovács, Z.; Ninsawat, S.; Bertalan, L.; Balázs, B.; Szabó, S. Identification of Roofing Materials with Discriminant Function Analysis and Random Forest Classifiers on Pan-Sharpened WorldView-2 Imagery—A Comparison. Hung. Geogr. Bull. 2018, 67, 375–392. [Google Scholar] [CrossRef] [Green Version]
- Samsudin, S.H.; Shafri, H.Z.M.; Hamedianfar, A. Development of Spectral Indices for Roofing Material Condition Status Detection Using Field Spectroscopy and WorldView-3 Data. J. Appl. Remote Sens. 2016, 10, 025021. [Google Scholar] [CrossRef]
- Abbasi, M.; Mostafa, S.; Vieira, A.S.; Patorniti, N.; Stewart, R.A. Mapping Roofing with Asbestos-Containing Material by Using Remote Sensing Imagery and Machine Learning-Based Image Classification: A State-of-the-Art Review. Sustainability 2022, 14, 8068. [Google Scholar] [CrossRef]
- Wu, P.-Y.; Mjörnell, K.; Sandels, C.; Mangold, M. Machine Learning in Hazardous Building Material Management: Research Status and Applications. Recent Prog. Mater. 2021, 3, 17. [Google Scholar] [CrossRef]
- Souffer, I.; Sghiouar, M.; Sebari, I.; Zefri, Y.; Hajji, H.; Aniba, G. Automatic Extraction of Photovoltaic Panels from UAV Imagery with Object-Based Image Analysis and Machine Learning. In Lecture Notes in Electrical Engineering Volume 745; Springer: Singapore, 2022; pp. 699–709. ISBN 9789813368927. [Google Scholar]
- Banolia, C.; Deshpande, S.; Balamuralidhar, P. Industrial/Metal Roof Detection from Hyperspectral Image in an Urban Scene. In Proceedings of the Remote Sensing Technologies and Applications in Urban Environments VII, Berlin, Germany, 26 October 2022; Chrysoulakis, N., Erbertseder, T., Zhang, Y., Eds.; p. 21. [Google Scholar]
- Wyard, C.; Beaumont, B.; Grippa, T.; Nys, G.-A.; Fauvel, H.; Hallot, É. Mapping Roof Materials Using WV3 Imagery and a State-of-the-Art OBIA Processing Chain: Application over Liège, Belgium. In Proceedings of the ESA Living Planet Symposium 2022, Bonn, Germany, 23–27 May 2022. [Google Scholar]
- StatBel. Statistique Cadastrale du Parc de Bâtiments, Belgique et Régions, 2022. Available online: https://bestat.statbel.fgov.be/bestat/crosstable.xhtml?view=43d7cdce-3647-4f5c-86f1-a4e0c864f692 (accessed on 9 February 2023).
- Beck, H.E.; Zimmermann, N.E.; McVicar, T.R.; Vergopolan, N.; Berg, A.; Wood, E.F. Present and Future Köppen-Geiger Climate Classification Maps at 1-Km Resolution. Sci. Data 2018, 5, 180214. [Google Scholar] [CrossRef] [Green Version]
- Heiden, U.; Segl, K.; Roessner, S.; Kaufmann, H. Determination of Robust Spectral Features for Identification of Urban Surface Materials in Hyperspectral Remote Sensing Data. Remote Sens. Environ. 2007, 111, 537–552. [Google Scholar] [CrossRef] [Green Version]
- Krówczyńska, M.; Wilk, E.; Pabjanek, P.; Kycko, M. Hyperspectral Discrimination of Asbestos-cement Roofing. Geomat. Environ. Eng. 2017, 11, 47. [Google Scholar] [CrossRef] [Green Version]
- Le Bris, A.; Chehata, N.; Briottet, X.; Paparoditis, N. Spectral Band Selection for Urban Material Classification Using Hyperspectral Libraries. In Proceedings of the 23. ISPRS Congress, International Society for Photogrammetry and Remote Sensing (ISPRS). INT., Prague, Czech Republic, 12–19 July 2016; Volume 3. [Google Scholar] [CrossRef] [Green Version]
- Long, Y.; Rivard, B.; Rogge, D.; Tian, M. Hyperspectral Band Selection Using the N-Dimensional Spectral Solid Angle Method for the Improved Discrimination of Spectrally Similar Targets. Int. J. Appl. Earth Obs. Geoinf. 2019, 79, 35–47. [Google Scholar] [CrossRef]
- Braun, A.; Warth, G.; Bachofer, F.; Hochschild, V. Identification of Roof Materials in High-Resolution Multispectral Images for Urban Planning and Monitoring. In Proceedings of the 2019 Joint Urban Remote Sensing Event (JURSE), Vannes, France, 22–24 May 2019; pp. 1–4. [Google Scholar]
- Wan, Z.; Ng, D.; Dozier, J. Spectral Emissivity Measurements of Land-Surface Materials and Related Radiative Transfer Simulations. Adv. Sp. Res. 1994, 14, 91–94. [Google Scholar] [CrossRef]
- Snyder, W.C.; Wan, Z. BRDF Models to Predict Spectral Reflectance and Emissivity in the Thermal Infrared. IEEE Trans. Geosci. Remote Sens. 1998, 36, 214–225. [Google Scholar] [CrossRef] [Green Version]
- Herold, M.; Roberts, D.A.; Gardner, M.E.; Dennison, P.E. Spectrometry for Urban Area Remote Sensing—Development and Analysis of a Spectral Library from 350 to 2400 Nm. Remote Sens. Environ. 2004, 91, 304–319. [Google Scholar] [CrossRef]
- Kokaly, R.F.; Clark, R.N.; Swayze, G.A.; Livo, K.E.; Hoefen, T.M.; Pearson, N.C.; Wise, R.A.; Benzel, W.M.; Lowers, H.A.; Driscoll, R.L.; et al. USGS Spectral Library Version 7; U.S. Geological Survey: Reston, VA, USA, 2017. [Google Scholar]
- Baldridge, A.M.; Hook, S.J.; Grove, C.I.; Rivera, G. The ASTER Spectral Library Version 2.0. Remote Sens. Environ. 2009, 113, 711–715. [Google Scholar] [CrossRef]
- Meerdink, S.K.; Hook, S.J.; Roberts, D.A.; Abbott, E.A. The Ecostress Spectral Library Version 1.0. Remote Sens. Environ. 2019, 230, 111196. [Google Scholar] [CrossRef]
- Ben-Dor, E.; Levin, N.; Saaroni, H. A Spectral Based Recognition of the Urban Environment Using the Visible and Near-Infrared Spectral Region (0.4-1.1 Μm). A Case Study over Tel-Aviv, Israel. Int. J. Remote Sens. 2001, 22, 2193–2218. [Google Scholar] [CrossRef]
- Heiden, U.; Roessner, S.; Segl, K.; Kaufmann, H. Analysis of Spectral Signatures of Urban Surfaces for Their Identification Using Hyperspectral HyMap Data. In Proceedings of the IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas (Cat. No.01EX482), Rome, Italy, 8–9 November 2001; pp. 173–177. [Google Scholar]
- Ilehag, R.; Schenk, A.; Huang, Y.; Hinz, S. KLUM: An Urban VNIR and SWIR Spectral Library Consisting of Building Materials. Remote Sens. 2019, 11, 2149. [Google Scholar] [CrossRef] [Green Version]
- Sobrino, J.A.; Oltra-Carrió, R.; Sòria, G.; Bianchi, R.; Paganini, M. Impact of Spatial Resolution and Satellite Overpass Time on Evaluation of the Surface Urban Heat Island Effects. Remote Sens. Environ. 2012, 117, 50–56. [Google Scholar] [CrossRef]
- Nasarudin, N.E.M.; Shafri, H.Z.M. Development and Utilization of Urban Spectral Library for Remote Sensing of Urban Environment. J. Urban Environ. Eng. 2011, 5, 44–56. [Google Scholar] [CrossRef]
- Kotthaus, S.; Smith, T.E.L.; Wooster, M.J.; Grimmond, C.S.B. Derivation of an Urban Materials Spectral Library through Emittance and Reflectance Spectroscopy. ISPRS J. Photogramm. Remote Sens. 2014, 94, 194–212. [Google Scholar] [CrossRef] [Green Version]
- Zambrano-Prado, P.; Josa, A.; Rieradevall, J.; Pérez-Aragüés, F.; Marchan, J.F.; Gassó-Domingo, S.; Gabarrell, X. Laboratory-Based Spectral Data Acquisition of Roof Materials. Int. J. Remote Sens. 2020, 41, 9180–9205. [Google Scholar] [CrossRef]
- Kalacska, M.; Arroyo-Mora, J.P.; Soffer, R.J.; Elmer, K. ASD FieldSpec3 Field Measurement Protocols. Protocols.io. 2019. Available online: https://dx.doi.org/10.17504/protocols.io.qu7dwzn (accessed on 20 December 2022).
- Soffer, R.J.; Ifimov, G.; Arroyo-Mora, J.P.; Kalacska, M. Validation of Airborne Hyperspectral Imagery from Laboratory Panel Characterization to Image Quality Assessment: Implications for an Arctic Peatland Surrogate Simulation Site. Can. J. Remote Sens. 2019, 45, 476–508. [Google Scholar] [CrossRef]
- Elmer, K.; Soffer, R.J.; Arroyo-Mora, J.P.; Kalacska, M. ASDToolkit: A Novel MATLAB Processing Toolbox for ASD Field Spectroscopy Data. Data 2020, 5, 96. [Google Scholar] [CrossRef]
- HSE (Health and Safety Executive). Asbestos: The Analysts’ Guide, 2nd ed.; TSO (The Stationary Office): Norwich, UK, 2021; 240p, ISBN 9780616667079. [Google Scholar]
Reference | Study Area | Spectral Range [nm] | Instrument | Data Acquisition | Number of Roof Material Spectra |
---|---|---|---|---|---|
Wan et al. [18]; Snyder et al. [19] MODIS UCSB Emissivity Library | Various localizations, USA | 3300–14,500 | MIDAC M2510-C FTIR | Laboratory | 18 |
Herold et al. [20] Santa Barbara (SB) spectral library | Santa Barbara, CA, USA | 350–2500 | ASD FieldSpec3 | In situ | 3 |
Kokaly et al. [21] USGS v.7 | Various localizations, USA | 350–2500 | ASD FieldSpec3 | Laboratory | 16 |
Baldridge et al. [22]; Meerdink et al. [23] ASTER 2.0, ECOSTRESS 1.0 | Various localizations, USA | 400–15,400 | Perkin–Elmer Lambda 900UV/VIS/NIR, Perknic-Nicolet 520 FTIR | Laboratory | 18 |
Ben-Dor et al. [24] | Tel-Aviv, Israël | 430–940 | CASI, ASD FieldSpec3 | Airborne In situ | 2 |
Heiden et al. [13,25] | Dresden and Potsdam, Germany | 350–2500 | HyMap and ASD FieldSpec3 | Airborne In situ | 13 |
Ilehag et al. [26] KLUM | Karlsruhe, Germany | 350–2500 | ASD FieldSpec-4 Hi-Res | In situ | 38 |
Sobrino [27] DESIREX | Madrid, Spain | 350–2500 | Airborne Hyperspectral Scanner (AHS), ASD FieldSpec3, GER | Airborne In situ | 1 |
Nasarudin and Shafri [28] | Campus of university, Malaysia | 350–2500 | ASD FieldSpec3 | In situ | 15 |
Kotthaus et al. [29] LUMA SLUM | London, UK | 350–15,400 | HR-1024 field spectro-radiometer, M200 FTIR | Laboratory | 30 |
Zambrano-Prado et al. [30] | Mediterranean regions | 400–1000 | AISA Eagle 2 sensor | Laboratory | 39 |
WaRM | Various localizations, Wallonia, Belgium | 350–2500 | ASD FieldSpec3 Hi-Res | Laboratory In situ | 26 |
Roof Material | Class | Number of Samples | Degree of Weathering |
---|---|---|---|
Aluminium—Black painting | Metal | 1 | Low |
Aluminum—Grey painting | Metal | 1 | Low to medium |
Aluminum—Natural | Metal | 1 | Low |
Artificial slate with asbestos—Brown color | Slate | 6 | Medium |
Artificial slate without asbestos—Anthracite color | Slate | 4 | Low |
Artificial slate without asbestos—Brown color | Slate | 1 | Low |
Artificial slate without asbestos—Light gray color | Slate | 2 | Low |
Artificial slate without asbestos—Purple color | Slate | 1 | Low |
Bitumen membrane—Black color | Membrane | 3 | Low to medium |
Ceramic tile—Black color | Tile | 8 | Low to high |
Ceramic tile—Brown color | Tile | 4 | Low |
Ceramic tile—Orange color | Tile | 6 | Low to high |
Fiberglass roofing panel—Translucent and corrugated | PVC | 1 | High |
Concrete tile—Black color | Tile | 3 | Low to medium |
Concrete tile—Brown color | Tile | 1 | Low |
Corrugated asbestos cement sheet | Corrugated cement sheet | 2 | Medium |
EPDM membrane—Black color | Membrane | 1 | Low |
Lead—Natural | Metal | 2 | Medium |
Natural slate—Anthracite color | Slate | 2 | Low to medium |
Natural slate—Purple color | Slate | 2 | Low |
Photovoltaic solar panel | Solar panel | 1 | Medium |
Polycarbonate panel—Twin wall | PVC | 1 | Low |
Steel—Black painting | Metal | 2 | Low to medium |
Steel—Grey painting | Metal | 1 | Low to medium |
Steel—Orange painting | Metal | 1 | Low |
Zinc—Natural | Metal | 4 | Low to high |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Wyard, C.; Marion, R.; Hallot, E. WaRM: A Roof Material Spectral Library for Wallonia, Belgium. Data 2023, 8, 59. https://doi.org/10.3390/data8030059
Wyard C, Marion R, Hallot E. WaRM: A Roof Material Spectral Library for Wallonia, Belgium. Data. 2023; 8(3):59. https://doi.org/10.3390/data8030059
Chicago/Turabian StyleWyard, Coraline, Rodolphe Marion, and Eric Hallot. 2023. "WaRM: A Roof Material Spectral Library for Wallonia, Belgium" Data 8, no. 3: 59. https://doi.org/10.3390/data8030059