Localized urbogeosystemic analsis through lidar data for formalized urban population estimation
Abstract
Our paper makes focus on the further research of the urban geosystem approach potential in the domain of social-geographical research through the combined application of GIS tools and the results of urban remote sensing (URS). The challenges of urban studies demand innovative methods for estimating population, which can be based on the building geometry and the architectural morphology of the city reconstructed on the URS base.
Proceeding from this, the aim of the paper is to represent localized urban geosystem analysis (LUGA), which is implemented on the largest geospatial scale of the given UGS. LUGA includes the use of area-metric (AMM) and volume-metric methods (VMM) for calculating the population in urban buildings and, thus, in a certain parcel of urbanized geospace. The latter can be considered the smallest structural unit of the detailed-grid representation of the digital urbanized environment (UE).
This study corresponds to one of the main postulates of urban geosystem analysis, according to which the formalization of UGS attributive characteristics occurs in various geolocations of the UE. The existing theoretical prerequisites of LUGA have been considered. Based on previous research experience, a thesis description of three alternative methods for assessing urban population distribution based on the "RSóGIS" paradigm has been proposed. Regarding the M1 LUGA technique, which is a further development of "micro-spatial GIS analysis," and its two parametric methods (AMM and VMM), a detailed description of their operational sequence and formalized apparatus have been provided. A block diagram of the step-by-step implementation of both methods is presented with detailed explanations of each stage. An example of LUGA implementation concerning a user scenario for assessing the distribution of urban population in the Boston agglomeration (Massachusetts, USA) has been provided. Pictures of the Cloud GIS-platform sample interface have been presented.
Downloads
References
Kostrikov, S., Niemets, L., Sehida, K. [and other]. (2018). Geoinformation approach to the urban geographic system research (case studies of Kharkiv region). Visnyk of V.N. Karazin Kharkiv National University. Series “Geology. Geography. Ecology”, 49, 107-124. https://doi.org/10.26565/2410-7360-2018-49-09
Kostrikov, S., Pudlo, R., Kostrikova, A., & Bubnov, D. (2019). Studying of urban features by the multifunctional approach to LiDAR data processing. Joint Urban Remote Sensing Event (JURSE), Vannes, France. 1-4. https://doi.org/10.1109/JURSE.2019.8809063
Kostrikov, S. (2019). Urban remote sensing with LIDAR for the Smart City concept implementation. Visnyk of V.N. Karazin Kharkiv National University. Series “Geology. Geography. Ecology”, 50, 101-124. https://doi.org/10.26565/2410- 7360-2019-50-08
Kostrikov, S., Bubnov, D., Kostrikova, A., & Pudlo, R. (2018). ELiT web-application – the software for urban environment modeling and analysis. GIS Forum – 2018, 55-59.
Serohin, D., & Kostrikov, S. (2023). Towards urbanistic geosituation delineation. Visnyk of V.N. Karazin Kharkiv National University. Series “Geology. Geography. Ecology”, 58, 241-256. https://doi.org/10.26565/2410-7360-2023-58-19 [in Ukrainian].
Serohin, D., & Kostrikov, S. (2023). Spatial assessment of buildings energy consumption based on three-dimensional modeling of the urban environment. Human Geography Journal, 34, 27-41. https://doi.org/10.26565/2076-1333-2023-34-01
Kostrikov, S., Serohin, D., & Berezhnoy, V. (2021). Visibility analysis of the urbanistic environmet as a constituent of the urbogeosystems approach. Human Geography Journal, 30(1), 7-23. https://doi.org/10.26565/2076-1333-2021- 30-01 [in Ukrainian].
Sampath, A., & Shan, J. (2010). Segmentation and reconstruction of polyhedral building roofs from aerial LIDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing. 48(3), 1554-1567. http://dx.doi.org/10.1109/TGRS.2009.2030180
Lozić, E. (2021). Application of airborne LiDAR data to the archaeology of agrarian land use: the case study of the early medieval microregion of Bled (Slovenia). Remote Sens, 13, 3228. https://doi.org/10.3390/rs13163228
Morar, C., Lukic, T., Valjarevic, A., Niemets, L., Kostrikov, S., Sehida, K., Telebienieva, I., Kluchko, L., Kobylin, P., & Kravchenko, K. (2022). Spatiotemporal Analysis of Urban Green Areas Using Change Detection: A Case Study of Kharkiv, Ukraine. Front. Environ. Sci, 10, 823129. https://doi.org/10.3389/fenvs.2022.823129
Loh, H., James, D., Ioki, K., Wong, W., Tsuyuki, S., & Phua, M.H. (2021). Estimating above-ground biomass changes in a human-modified tropical montane forest of Borneo using multi-temporal airborne LiDAR data. Remote Sens. Appl.: Soc. Environ, 28, 100821. https://doi.org/10.1016/j.rsase.2022.100821
Desthieux, G., Carneiro, C., Camponovo, R., Ineichen, P., Morello, E., Boulmier, A., Abdennadher, N., Dervey, S., & Ellert C. (2018). Solar Energy Potential Assessment on Rooftops and Facades in Large Built Environments Based on LiDAR. Data, Image Processing, and Cloud Computing. Methodological Back-ground, Application, and Validation in Geneva (Solar Cadaster). Front. Built Environ, 4. https://doi.org/10.3389/fbuil.2018.00014
Bakuła, K., Pilarska, M., Ostrowski, W., Nowicki, A., & Kurczyński, Z. (2020). UAV LiDAR data processing: influence of flight height on geometric accuracy, radiometric information and parameter setting in DTM production. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XLIII-B1, 21-26. https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-21-2020
Massoud, A., Fahmy, A., Iqbal, U., Givigi, S., & Noureldin, A. (2023). Real-time safe landing zone identification based on Airborne LiDAR. Sensors, 23(7), 3491. https://doi.org/10.3390/s23073491
Maas, H., & Vosselman, G. (1999). Two algorithms for extracting building models from raw laser altimetry data. ISPRS J. Photogramm. Remote Sens, 54, 153-163. http://dx.doi.org/10.1016/S0924-2716(99)00004-0
Kim, K., & Shan, J. (2011). Building footprints extraction of dense residential areas from LiDAR data. Ann. Conf. Am. Soc. Photogram. Remote Sens, WI, 193-198.
Maltezos, E., & Ioannids, C. (2016). Automatic extraction of building roofs from Airborne LiDAR data applying and extended 3D randomized Hough transform. ISPRS Annals, III-3, 209-216. http://dx.doi.org/10.5194/isprs-annals-III-3-209-2016
Kada, M. (2022). 3D reconstruction of simple buildings from point clouds using neural networks with continuous convolutions (convpoint). Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XLVIII-4/W4-2022, 61-66. https://doi.org/10.5194/isprs-archives-XLVIII-4-W4-2022-61-2022
Yanga, B., Haalab, N., & Donga, Z. (2023). Progress and perspectives of point cloud intelligence. Geospatial information science, 26(2), 189-205. https://doi.org/10.1080/10095020.2023.2175478
Kostrikov, S., & Seryogin, D. (2022). Urbogeosystemic Approach to Agglomeration Study within the Urban Remote Sensing Frameworks. Urban Agglomeration. Edited by A. Battisti and S. Baiani: Intech Open, London, Milan, Zagreb. 251-273. http://dx.doi.org/10.5772/intechopen.102482
Harvey, J.T. (2002). Population estimation models based on individual TM pixels. Photogrammetric Engineering and Remote Sensing, 68, 1181-1192.
Dong, P., Ramesh, S., & Nepali, A. (2010). Evaluation of small area population estimation using LiDAR, Landsat TM and parcel data. International Journal of Remote Sensing, 31, 5571-5586. http://dx.doi.org/10.1080/01431161.2010.496804
Smith, S.K., & Mandell, M. (1984). A comparison of population estimation methods: housing unit versus component II, ratio correlation and administrative records. Journal of American Statistical Association, 79, 282-289. http://dx.doi.org/10.1080/01621459.1984.10478042
Lwin, K., & Murayama, Y. (2009). A GIS approach to estimation of building population for micro-spatial analysis. Transaction in GIS, 13(4), 401-414. http://dx.doi.org/10.1111/j.1467-9671.2009.01171.x
Lo, C.P. (2008). Population estimation using geographically weighted regression. Journal of GIScience and Remote Sensing, 45, 131-148. https://doi.org/10.2747/1548-1603.45.2.131
Kostrikov, S., Kravchenko, K., Serohin, D., Bilianska, S., & Savchenko, A. (2023). The performance of the digital city projects in urban studies of the megalopolises (the case studies of Kharkiv and Dnipro cities). Visnyk of V. N. Karazin Kharkiv National University, Series "Geology. Geography. Ecology”, 59, 140-165. https://doi.org/10.26565/2410-7360-2023-59-11
Mashhoodi, B., Stead, D., & van Timmeren A. (2019). Spatial homogeneity and heterogeneity of energy poverty: a neglected dimension. Annals of GIS, 25(1), 19-31. https://doi.org/10.1080/19475683.2018.1557253
Noppachai, W., Wongsai, S., Apiradee, L., McNeil, D., & Huete A.R. (2020). Impacts of spatial heterogeneity patterns on long-term trends of Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature time series. Journal of applied remote sensing, 14(1), 014513. https://dx.doi.org/10.1117/1.JRS.14.014513
Qui, F., & Woller, K.L. (2003). Modeling urban population growth from remotely sensed imagery and TIGER GIS Road Data. Photogrammetric Engineering and Remote Sensing, 69, 1031-1042. http://dx.doi.org/10.14358/PERS.69.9.1031
Kostrikov, S., Pudlo, R., Bubnov, D., & Vasiliev, V. (2020). ELiT, multifunctional web-software for feature extraction from 3D LiDAR point clouds. ISPRS International Journal of Geo-Information, 9(11), 650-885. http://dx.doi.org/10.3390/ijgi9110650
D.C. Open Source Data on Amazon S3. Retrieved from https://aws.amazon.com/ru/blogs/publicsector/lidar-data-for-washington-dc-is-available-as-an-aws-public-dataset/
Dobson, J.E. (2007). In harm's way: estimating populations at risk. In: Tools and Methods for Estimating Populations at Risk from Natural Disasters and Complex Humanitarian Crises. Edited by: National Research Council, Washington, DC, USA, The National Academies Press, 183-191.
Eicher, C.L., & Brewer, C.A. (2013). Dasymetric Mapping and Areal Interpolation: Implementation and Evaluation. Cartography and Geographic In-formation Science, 28(2), 125-138. https://doi.org/10.1559/152304001782173727
Menis, J. (2009). Dasymetric mapping for estimating population in small areas. Geography Compass, 3(2), 727-745. http://dx.doi.org/10.1111/j.1749-8198.2009.00220.x
Robinson, C., Hohman, F., & Dilkina B. (2017). A deep learning approach for population estimation from satellite imagery. Proceedings of the 1st ACM SIGSPATIAL Workshop on Geospatial Humanities, GeoHumani-ties’17, Association for Computing Machinery, New York, NY, USA. 47-54. https://dx.doi.org/10.1145/3149858.3149863
Zhuang, H., Liu, X., Yan, Y., Ou, J., He, J., & Wu, C. (2021). Mapping multi-temporal population distribution in China from 1985 to 2010 using Landsat im-ages via deep learning. Remote Sensing, 13, 3533. https://doi.org/10.3390/rs13173533
Lu, W., & Weng, Q. (2024). An ANN-based method for population Dasymetric mapping to avoid the scale heterogeneity: A case study in Hong Kong, 2016–2021. Computers, Environment and Urban Systems, 108, 102072. https://doi.org/10.1016/j.compenvurbsys.2024.102072
Wu, S., Qiu, X., & Wang L. (2005). Population estimation methods in GIS and remote sensing: A review. GIScience and Remote Sensing, 42, 80-96. https://doi.org/10.2747/1548-1603.42.1.80
Biljecki, F., Arroyo Ohori, K., Ledoux. H., Peters, R, & Stoter, J. (2016). Population Estimation Using a 3D City Model: A Multi-Scale Country-Wide Study in the Netherlands. PLOS ONE, 11(6), e01568. https://doi.org/10.1371/journal.pone.0156808
Kostrikov, S.V., Serohin, D.S., & Kravchenko, K.O. (2023). Workshop on creating GIS maps, spatial analysis and geoprocessing on full-format GIS platforms (using the example of ArcGIS 10.2 and QGIS 3.16): Educational and methodological manual for university students. Kharkiv, 499 [in Ukrainian].
MassGIS Data: 2010 U.S. Census. (2012). Mass.gov. Retrieved from https://www.mass.gov/info-details/massgis-data-2010-us-census
USGS Data. Retrieved from ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Elevation/LPC/Projects/USGS_LPC_MA_ Sndy_CMPG_2013_LAS_2015/laz/
Copyright (c) 2024 Kostrikov S., Serohin D.
This work is licensed under a Creative Commons Attribution 4.0 International License.
