Abstract

In the context of the feature-based categorization of residential land use, this research investigates the mutually beneficial relationship between remote sensing technologies and the features of settlement points. Through the incorporation of geographical data and information that is centered on people, this method provides a holistic knowledge of the dynamics of urban environments. In this work, a technique is presented that makes use of an existing database of settlement points identifying buildings in order to differentiate between regular and irregular residential settlements. Nine data characteristics that describe the density, distance, angles, and spacing of the settlement points are computed at several spatial scales. These features pertain to the settlement points. For the purpose of classifying land use zones, these data are examined both alone and in conjunction with five standard remote sensing metrics on elevation, slope, vegetation, and nighttime lights. This is done using a supervised machine learning technique.

References

1. Borana, s.L. & Yadav, s.K. & S.K.Parihar, & Paturkar, R. (2013). Integration of Remote Sensing & GIS for Urban Land Use / Cover Change Analysis of the Jodhpur city.
2. Hu, Shougeng & Wang, Le. (2013). Automated urban land-use classification with remote sensing. International Journal of Remote Sensing. 34. 790-803. 10.1080/01431161.2012.714510.
3. Huang, Huiping & Li, Qiangzi & Zhang, Yuan. (2019). Urban Residential Land Suitability Analysis Combining Remote Sensing and Social Sensing Data: A Case Study in Beijing, China. Sustainability. 11. 2255. 10.3390/su11082255.
4. Liu, Xiaoping & He, Jialv & Yao, Yao & Zhang, Jinbao & Liang, Haolin & Wang, Huan & Hong, Ye. (2017). Classifying urban land use by integrating remote sensing and social media data. International Journal of Geographical Information Science. 31. 10.1080/13658816.2017.1324976.
5. Rozenstein, Offer & Karnieli, Arnon. (2011). Comparison of methods for land-use classification incorporating remote sensing and GIS inputs. Applied Geography. 31. 533-544. 10.1016/j.apgeog.2010.11.006.
6. Şimşek, Duygu & Kaya, Sinasi & Ipbuker, Cengizhan & Sertel, Elif. (2013). Determination of characteristic properties of rural residential areas using remotely sensed data. 34th Asian Conference on Remote Sensing 2013, ACRS 2013. 1. 642-649.
7. Waldhoff, Guido & Eichfuss, Silas & Bareth, G.. (2015). INTEGRATION OF REMOTE SENSING DATA AND BASIC GEODATA AT DIFFERENT SCALE LEVELS FOR IMPROVED LAND USE ANALYSES. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XL-3/W3. 85-89. 10.5194/isprsarchives-XL-3-W3-85-2015.
8. Yao, Yao & Yan, Xiaoqin & Luo, Peng & Liang, Yuyun & Ren, Shuliang & Hu, Ying & Han, Jian & Guan, Qingfeng. (2022). Classifying land-use patterns by integrating time-series electricity data and high-spatial-resolution remote sensing imagery. International Journal of Applied Earth Observation and Geoinformation. 106. 102664. 10.1016/j.jag.2021.102664.
9. Yin, Jiadi & Dong, Jinwei & Hamm, Nicholas & Li, Zhichao & Wang, Jianghao & Xing, Hanfa & Fu, Ping. (2021). Integrating remote sensing and geospatial big data for urban land use mapping: A review. International Journal of Applied Earth Observation and Geoinformation. 103. 102514. 10.1016/j.jag.2021.102514.
10. Yin, Jiadi & Fu, Ping & Hamm, Nicholas & Li, Zhichao & Nanshan, You & Yingli, He & Cheshmehzangi, Ali & Dong, Jinwei. (2021). Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping. Remote Sensing. 13. 1579. 10.3390/rs13081579.

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