An integrated semiautomated and transferable data driven approach for urban texture mapping

Year 2026, Volume: 11 Issue: 1, 183 - 211, 01.10.2025

Fatemeh Sanaei , Bakhtiar Feizizadeh , Amin Naboureh , Murat Yakar

https://doi.org/10.26833/ijeg.1681173

Abstract

Urbanization has significantly increased over the past decades, making monitoring of urban growth and urban texture essential for urban planning and sustainable development. In this context, the classification of different urban textures has gained importance, leveraging advancemnts insatellite image processing and methods such as machine learning and object-based image analysis (OBIA), as well as their integration. The present study aims to apply and evaluate different object-based methods to map urban texture in different part of Tabriz city in Iran. To this end, five area with distinct urban texture patterns were selected and analyzed using OBIA’s spectral and spatial features. A semiautomated OBIA approach was developed and applied to map urban textures, and its robustness and efficiency was examinedOur analysis indicated that combining the average, shape, and gray level co-occurrence matrix methods enhances the ability to identify objects in urban environments. The results highlight the high potential of OBIA algorithms and features in detecting and classifying urban areas. This study provides valuable insights for urban planners, offering a useful tool for informed decision-making in future urban development.

Keywords

Different patterns of urban texture , Worn-out texture , Object-based image analysis , Spectral and spatial algorithms

References

• Feizizadeh, B., Blaschke, T., Tiede, D., & Rezaei Moghaddam, M. H. (2017). Evaluation of fuzzy operators within an object-based image analysis approach for landslide change detection analysis. Geomorphology, 293, 240–254. https://doi.org/10.1016/j.geomorph.2017.06.012
• Sim, L. L. (1996). Urban conservation policy and the municipality in their rehabilitation: Case study of Khoy city, Khamachilar neighborhood. Shebak, 7(1), 125–142.
• Batty, M. (1980). Reviewed works: Urban land use planning by F. Stuart Chapin Jr., & Edward J. Kaiser; Hypothetical city exercise. The Town Planning Review, 51(4), 473–475.
• Davoudpour, Z., & Niknia, M. (2011). Renovation of worn-out urban texture towards achieving the physical dimensions of sustainable urban development: Case study of worn-out texture of Sajjadieh alley. Environmental-Based Territorial Planning Quarterly, 4(15), 31–51.
• Adibi Sadinezhad, F. (2010). The concept of worn texture and its characteristics. Monthly Councils, 5(54).
• Valizadeh, K., & Alipor, F. (2014). Investigation and zoning of the criteria for identifying worn-out fabric in the metropolis of Tabriz. In Conference on Worn-Out and Historical Urban Fabrics: Challenges and Solutions.
• Mahdizadeh, J. (2004). The place of cultural heritage in sustainable urban development. Urban Planning Inquiries, 12, 14–25.
• Kropf, K. (2018). The handbook of urban morphology. Chichester, UK: John Wiley & Sons Ltd.
• Moudon, A. V. (1997). Urban morphology as an emerging interdisciplinary field. Urban Morphology, 1(1), 3–10.
• Kang, J. E., et al. (2019). Evaluating the effect of compact urban form on air quality in Korea. Environment and Planning B: Urban Analytics and City Science, 46(1), 179–200. https://doi.org/10.1177/2399808317705880
• Yuan, M., et al. (2018). Exploring the association between urban form and air quality in China. Journal of Planning Education and Research, 38(4), 413–426. https://doi.org/10.1177/0739456X17711516
• Rode, P., et al. (2014). Cities and energy: Urban morphology and residential heat-energy demand. Environment and Planning B: Planning and Design, 41(1), 138–162. https://doi.org/10.1068/b39065
• Liu, Z., et al. (2017). Neighborhood-scale urban form, travel behavior, and CO2 emissions in Beijing: Implications for low-carbon urban planning. Urban Geography, 38(3), 381–400. https://doi.org/10.1080/02723638.2016.1191796
• Yang, W., et al. (2015). Examining the impacts of socio-economic factors, urban form and transportation development on CO2 emissions from transportation in China: A panel data analysis of China’s provinces. Habitat International, 49, 212–220. https://doi.org/10.1016/j.habitatint.2015.05.030
• Rybarczyk, G., & Wu, C. (2014). Examining the impact of urban morphology on bicycle mode choice. Environment and Planning B: Planning and Design, 41(2), 272–288. https://doi.org/10.1068/b37133
• Tang, L., et al. (2018). Exploring the influence of urban form on urban vibrancy in Shenzhen based on mobile phone data. Sustainability, 10(12). https://doi.org/10.3390/su10124565
• Cheng, J. (2011). Exploring urban morphology using multi-temporal urban growth data: A case study of Wuhan, China. Asian Geographer, 28(2), 85–103. https://doi.org/10.1080/10225706.2011.623399
• Milad, M. A., et al. (2018). Monitoring and assessment of urban growth patterns using spatio-temporal built-up area analysis. Environmental Monitoring and Assessment, 190(3), 1–26. https://doi.org/10.1007/s10661-018-6522-9
• Carolina, Q. R., & Felipe, J. (2021). Urban fabrics to eco-friendly blue–green for urban wetland development. Sustainability, 13, 13745. https://doi.org/10.3390/su132413745
• Tong, M. (2014). How urban fabric can help sustain the vitality of cities. Urban Planning Forum, 216, 85–96.
• Li, X., Cheng, S., Lv, Z., Song, H., Jia, T., & Lu, N. (2020). Data analytics of urban fabric metrics for smart cities. Future Generation Computer Systems, 107, 871–882. https://doi.org/10.1016/j.future.2020.02.027
• Leila, A. (2022). The semantic value of historical monuments as cultural heritage in urban texture: Cases of Ankara Castle and Anıtkabir. Journal of Architectural Conservation, 28, 197–216. https://doi.org/10.1080/13556207.2021.1998065
• Pont, M. B., & Haupt, P. (2005). The spacemate: Density and the typomorphology of the urban fabric. In Urbanism Laboratory for Cities and Regions: Progress of Research Issues in Urbanism. Amsterdam, The Netherlands: IOS Press.
• Cohen, N. (2004). Urban planning conservation and preservation (S. Wang, Trans.). Beijing, China: China Machine Press.
• Cataldi, G., Maffei, G., & Vaccaro, P. (2002). Saverio Muratori and the Italian school of planning typology. Urban Morphology, 6, 3–14.
• Whitehand, J. (2003). From Como to Alnwick: In pursuit of Caniggia and Conzen. Urban Morphology, 7, 69–72.
• Faizizadeh, B., & Salmani, S. (2016). Modeling agricultural land degradation due to urban growth and development by applying object-based methods of satellite image processing in the urban area of Urmia. Ameish Sarzemin Magazine, 8(2), 177–202.
• Aguirre-Gutiérrez, J. C., Seijmonsbergen, A. F., & Duivenvoorden, J. F. (2012). Optimizing land cover classification accuracy for change detection: A combined pixel-based and object-based approach in a mountainous area in Mexico. Applied Geography, 34, 29–37. https://doi.org/10.1016/j.apgeog.2011.10.010
• Feizizadeh, B., Kazamei, M., Blaschke, T., & Lakes, T. (2021). An object-based image analysis applied for volcanic and glacial landforms mapping in Sahand Mountain, Iran. Catena, 198, 105073. https://doi.org/10.1016/j.catena.2020.105073
• Hossain, M., & Chen, D. (2019). Segmentation for object-based image analysis (OBIA): A review of algorithms and challenges from a remote sensing perspective. ISPRS Journal of Photogrammetry and Remote Sensing, 150, 115–134. https://doi.org/10.1016/j.isprsjprs.2019.02.013
• Omarzadeh, D., Karimzadeh, S., Matsuoka, M., & Feizizadeh, B. (2021). Earthquake aftermath from very high-resolution WorldView-2 image and semi-automated object-based image analysis: Case study of Kermanshah, Sarpol-e Zahab, Iran. Remote Sensing, 13, 4272. https://doi.org/10.3390/rs13214272
• Liu, T., Yang, L., & Lunga, D. (2021). Change detection using deep learning approach with object-based image analysis. Remote Sensing of Environment, 256, 112308. https://doi.org/10.1016/j.rse.2021.112308
• Masayu, N., Hanani Mohd, S., Zuraihan, M., Ashnita, R., Fazly, A. M., & Helmi Zulhaidi Mohd, S. (2021). Urban building detection using object-based image analysis (OBIA) and machine learning (ML) algorithms. IOP Conference Series: Earth and Environmental Science, 620(1), 012010. https://doi.org/10.1088/1755-1315/620/1/012010
• Feizizadeh, B. , Kazemi, S. and Sahrafei, S. (2018). A Semi-Automated Approach for Identifying and Classifying Urban Old and Modern Textures Based on Spectral and Spatial Patterns in Object-Oriented Remote Sensing (Case Study: Isfahan City). Human Geography Research, 50(3), 661-678. https://doi:10.22059/jhgr.2017.204379.1007209
• Iran Statistics Center. (2011). General census of Iran's housing population.
• Kheyroddin, R., Imani, J., & Forouhar, A. (2013). An analysis of spatial consequences of urban management measures in old and new sections of the metropolis of Tabriz. IUESA, 1(4), 93–109. http://iueam.ir/article-1-51-en.html
• Safori, S., Babayi, B., & Norozi Sani, P. (2022). Evaluation and recognition of vital drivers effective in the urban regeneration of worn-out tissues with a future research approach: Case example of Hasht District of Tabriz. Scientific-Research Quarterly of Geography and Regional Planning, 12(49), 411–429.
• Babrafkan, S., & Roshanayi Badr, T. (2017). Investigating the effective methods in revitalizing the worn-out fabric of Tabriz city. In Proceedings of the National Conference on Applied Research in Civil Engineering, Architecture, and Urbanism.
• Blaschke, T., Feizizadeh, B., & Hölbling, D. (2014). Object-based image analysis for landslide delineation: Integrating information from remote sensing images and digital elevation models. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(12), 4806–4817. https://doi.org/10.1109/JSTARS.2014.2321330
• Dragut, L., & Blaschke, T. (2006). Automated classification of landform elements using object-based image analysis. Geomorphology, 81(3–4), 330–344. https://doi.org/10.1016/j.geomorph.2006.04.004
• Yakar, M. (2009). Digital elevation model generation by robotic total station instrument. Experimental Techniques, 33(2), 52-59.
• Nadiri, A. A., Khatibi, R., Khalifi, P., & Feizizadeh, B. (2020). A study of subsidence hotspots by mapping vulnerability indices through innovatory ‘ALPRIFT’ using artificial intelligence at two levels. Bulletin of Engineering Geology and the Environment, 79(7), 3989–4003. https://doi.org/10.1007/s10064-020-01782-3
• Zhou, W., & Troy, A. (2008). An object-based approach for analysing and characterizing urban landscape at the parcel level. International Journal of Remote Sensing, 29(11), 3119–3135. https://doi.org/10.1080/01431160701469065
• Bunting, P., Clewley, D., Lucas, R. M., & Gillingham, S. (2014). The Remote Sensing and GIS Software Library (RSGISLib). Computers & Geosciences, 62, 216–226. https://doi.org/10.1016/j.cageo.2013.08.007
• De Chant, T., & Kelly, M. (2009). Individual object change detection for monitoring the impact of a forest pathogen on a hardwood forest. Photogrammetric Engineering & Remote Sensing, 75(8), 1005–1013. https://doi.org/10.14358/PERS.75.8.1005
• Aksoy, B., & Ercanoglu, M. (2012). Landslide identification and classification by object-based image analysis and fuzzy logic: An example from the Azdavay region (Kastamonu, Turkey). Computers & Geosciences, 38, 87–98. https://doi.org/10.1016/j.cageo.2011.05.012
• Eisank, C., Smith, M., & Hillier, J. (2014). Assessment of multiresolution segmentation for delimiting drumlins in digital elevation models. Geomorphology, 214, 452–464. https://doi.org/10.1016/j.geomorph.2014.02.028
• Feizizadeh, B., Blaschke, T., Tiede, D., & Rezaei Moghaddam, M. H. (2017). Evaluation of fuzzy operators within an object-based image analysis approach for landslide change detection analysis. Geomorphology, 293, 240–254. https://doi.org/10.1016/j.geomorph.2017.05.019
• Gebejes, A., & Huertas, R. (2013, March 25–29). Texture characterization based on grey-level co-occurrence matrix. In Proceedings of the Conference of Informatics and Management Sciences (Zilina, Slovakia).
• Ghanbari, S., et al. (2011). Hiding in images using co-occurrence matrix and neural network. Iranian Electrical Engineering and Computer Engineering Journal, 9(3), 169–174.
• Hoseini, M., Najar Arabi, B., & Soltanianzadeh, H. (2007, February 14–15). The classification of iris color images with the co-occurrence matrix of hue image: A layer of hierarchical structure for the identification of resistant individuals. In Proceedings of the 4th Iranian Conference on Machine Vision and Image Processing (Mashhad University, Mashhad, Iran).
• Pesaresi, M., Gerhardinger, A., & Kayitakire, F. (2008). A robust built-up area presence index by anisotropic rotation-invariant textural measure. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1(3), 180–192. https://doi.org/10.1109/JSTARS.2008.2002869
• Pesaresi, M. (1999, October). Textural classification of very high-resolution satellite imagery: Empirical estimation of the interaction between window size and detection accuracy in urban environment. In Proceedings of the International Conference on Image Processing (Vol. 1, pp. 114–118). Kobe, Japan.
• Stehman, S. V. (2013). Estimating area from an accuracy assessment error matrix. Remote Sensing of Environment, 132, 202–211. https://doi.org/10.1016/j.rse.2013.01.016
• Zhou, W., & Troy, A. (2008). An object-based approach for analysing and characterizing urban landscape at the parcel level. International Journal of Remote Sensing, 29(11), 3119–3135. https://doi.org/10.1080/01431160701469065
• Story, M., & Congalton, R. G. (1986). Accuracy assessment: A user’s perspective. Photogrammetric Engineering & Remote Sensing, 52(3), 397–399. https://doi.org/10.14358/PERS.52.3.397
• Janssen, L. L. F., & Vanderwel, F. G. M. (1994). Accuracy assessment of satellite derived land cover data: A review. Photogrammetric Engineering & Remote Sensing, 60(4), 419–426. https://doi.org/10.14358/PERS.60.4.419
• Schneevoigt, N. J., van der Linden, S., Thamm, H.-P., & Schrott, L. (2008). Detecting Alpine landforms from remotely sensed imagery: A pilot study in the Bavarian Alps. Geomorphology, 93(1–2), 104–119. https://doi.org/10.1016/j.geomorph.2006.12.034
• Gerçek, D. (2010). Object-based classification of landforms based on their local geometry and geomorphometric context (Doctoral dissertation, Department of Geodetic and Geographic Information Technologies). Middle East Technical University.
• Orhan, O., & Yakar, M. (2016). Investigating land surface temperature changes using Landsat data in Konya, Turkey. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 41, 285-289.
• Bag, A., Sharma, A., & Pal, S. (2023). Studying urbanization pattern in Sambalpur City during 1992–2042 using CA-ANN, and Markov-Chain model. International Journal of Engineering and Geosciences, 9(3), 356–367. https://doi.org/10.26833/ijeg.1452005
• Bahadır, M., Ocak, F., & Şen, H. (2024). Determination of the development of settlements above earthquake susceptibility classes in Atakum district (Samsun/Türkiye). International Journal of Engineering and Geosciences, 9(3), 390–405. https://doi.org/10.26833/ijeg.1465072
• Feizizadeh, B., Shaabei, H., Dedehaban, K., Blaschke, T., & Neubauer, F. (2020). Object-based thermal remote-sensing analysis for fault detection in Mashhad County, Iran. Canadian Journal of Remote Sensing, 45(6), 847–861. https://doi.org/10.1080/07038992.2019.1704622
• Feizizadeh, B., Kazamei, M., Blaschke, T., & Lakes, T. (2021). An object-based image analysis applied for volcanic and glacial landforms mapping in Sahand Mountain, Iran. Catena, 196, 105073. https://doi.org/10.1016/j.catena.2020.105073
• Bagri, S., Karimzadeh, S.Feizizadeh, B., 2022. Investigation and modeling of physical development of urban areas and its effects on light pollution using night light data, International Journal of Engineering and Geosciences. https:// doi.org/10.26833/ijeg.976495.
• Khorrami, B., Gunduz, O., Patel, N., Ghouzlane, S., et al. (2019). Land surface temperature anomalies in response to changes in forest cover. International Journal of Engineering and Geosciences, 4(3), 149-156. https://doi.org/10.26833/ijeg.549944.
• Akbulut, Z., Özdemir, S., Acar, H., Dihkan, M., et al. (2018). Automatic extraction of building boundaries from high resolution images with active contour segmentation. International Journal of Engineering and Geosciences, 3(1), 36-42. https://doi.org/10.26833/ijeg.373152