Pattern Recognition Methods for Remote-Sensing-Based Land Use and Land Cover Analysis

Abstract

Pattern recognition is critical for obtaining useful informationfromremotesensingdataandimprovingland-use and land-cover (LULC) classification. With the increased availability of multispectral, hyperspectral, LiDAR, and high-resolution data, sophisticated algorithms are needed to address issues such spectral similarity, geographical heterogeneity, at-mospheric distortions, and mixed pixels. Recent research has shown that machine-learning models such as SVM, Random Forest, Ensemble Learning, and ELM, as well as deep-learning architectures such as CNNs, U-Net, and hybrid spectral-spatial networks, improve classification accuracy and environmental monitoring. Semantic alignment, open-vocabulary mapping, spatial point pattern analysis, and novel-class discovery are all emerging technologies that promote adaptability in dynamic contexts. The use of GIS, Monte Carlo simulations, and mul-timodal data fusion improves the modelling of environmental processesandlong-termchanges.Overall,thereviewedresearch demonstrate that sophisticated pattern-recognition approaches offerdependable,scalable,anddata-drivensolutionsforremote-sensing applications, thereby promoting sustainable resource management, urban planning, ecological conservation, and climate-resilient development.

Introduction

The text reviews advances in remote sensing and pattern recognition for land-use and land-cover (LULC) analysis, driven by the explosion of Earth observation data from satellites, LiDAR, and multispectral imaging. Traditional manual interpretation and pixel-based methods are no longer sufficient due to issues like spectral similarity, mixed pixels, and atmospheric noise. To address this, researchers increasingly use machine learning (SVM, Random Forest, ensemble models) and deep learning (CNNs, U-Net, transformers, hybrid models), which integrate spectral, spatial, temporal, and textural features for more accurate classification.

Recent work shows that combining these techniques with GIS, time-series analysis, and multimodal data fusion significantly improves environmental monitoring, enabling applications in urban planning, disaster management, ecological conservation, and climate studies. However, challenges remain, including limited labeled data, model transferability, class imbalance, and computational cost.

The literature survey highlights multiple studies demonstrating that:

• Hybrid and deep learning models outperform traditional classifiers in accuracy and robustness.
• Object-based and semantic-aware methods improve classification of complex land-cover types.
• New approaches like open-vocabulary learning, point-pattern analysis, and novel class discovery address limitations of fixed label systems.
• Integration of remote sensing with GIS and environmental indices (NDVI, SAVI, LST) enhances understanding of urban growth and environmental change.

Conclusion

This study emphasises how important pattern recognition isforincreasingremotesensingdataprocessingandland-use and land-cover (LULC) categorisation accuracy. It is clear from the reviewed works that combining spectral, spatial, textural, and temporal features with contemporary machine-learning and deep-learning techniques greatly improves classification performance, lowers noise, and facilitatesmore accurate interpretation of complex landscapes. The shortcomingsofconventionalclosed-setsystemsarefurther addressed by emerging techniques like semantic alignment, open-vocabularymodels,spatialpoint-patternanalysis,and novel-class discovery, which provide more flexibilityfor practical applications. Understanding environmental processes, radiation exposure, ecological interactions, and landscape dynamics is further improved by the integrationof GIS, Monte Carlo simulations, and multimodal data fusion. All things considered, recent developments in pattern recognition offer robust, scalable, and data-driven solutions for remote sensing analysis, promoting climate resilience, ecological conservation, sustainable urban development, and well-informeddecision-making.Theliterature’sobservations show that sustained innovation in this area will be crucial for addressing upcoming environmental issues and enhancingthe accuracy of LULC mapping.

References

[1] L. M. Fonseca, T. S. Ko¨rting,H. d. N. Bendini, C. D.Girolamo-Neto,A. K. Neves, A. R. Soares, E. C. Taquary, and R. V. Maretto, “Patternrecognitionandremotesensingtechniquesappliedtolanduseandlandcover mapping in the brazilian savannah,” Pattern recognition letters,vol. 148, pp. 54–60, 2021. [2] H. Xie and H. Huang, “Classification of land cover remote-sensingimages based on pattern recognition,” Scientific Programming, vol.2022, no. 1, p. 8319692, 2022. [3] T. U. Omali and K. Umoru, “Pattern recognition in remote sensingimage analysis: Theory, methods, and a case study of rijiyar donor inkatsina,nigeria,”Int.J.Sci.Res.inComputerScienceandEngineeringVol, vol. 11, no. 6, 2023. [4] X. Ma, Z. Wang, Y. Ishida, and H. Kobayashi, “Land-cover-basedindoor gamma-ray dose after a nuclear accident: location factor map-ping via monte carlo simulation, gis, and remote sensing,” Journal ofEnvironmental Radioactivity, vol. 291, p. 107853, 2026. [5] V. Zermatten, J. Castillo-Navarro, D. Marcos, and D. Tuia, “Learningtransferable land cover semantics for open vocabulary interactionswith remote sensing images,” ISPRS Journal of Photogrammetry andRemote Sensing, vol. 220, pp. 621–636, 2025. [6] J.Du,J.Zelek,D.Zhang,andJ.Li,“3dlcdm:Hybridsupervisionfor land cover discovery mapping of emerging urban structures in 3dremotesensing,”RemoteSensingofEnvironment,vol.331,p.115018,2025. [7] V. Sangeetha and L. Agilandeeswari, “Artificial intelligence enabledspectral-spatial feature extraction techniques for land use and landcover classification using hyperspectral images–an inclusive review,”The Egyptian Journal of Remote Sensing and Space Sciences, vol. 28,no. 3, pp. 455–467, 2025. [8] P.C. Toscan, K. Seong, J. Jiao, C.A. L. R. Ribeiro, F. A. C.Carvalho,M. L. Oliveira, and E. B. Pereira, “Impact of nature-based solutions(nbs) on urban surface temperatures and land cover changes usingremote sensing and machine learning,” Remote Sensing Applications:Society and Environment, p. 101721, 2025. [9] R. Kosarevych, O. Lutsyk, B. Rusyn, O. Alokhina, T. Maksymyuk,andJ.Gazda,“Spatialpointpatternsgenerationonremotesensingdatausing convolutional neural networks with further statistical analysis,”Scientific Reports, vol. 12, no. 1, p. 14341, 2022. [10] G.L.Alawode,T.V.Oluwajuwon,R.A.Hammed,K.E.Olasuyi,A. Krasovskiy, O. C. Ogundipe, and F. Kraxner, “Spatiotemporalassessmentoflanduselandcoverdynamicsinmo¨dlingdistrict,austria,using remote sensing techniques,” Heliyon, vol. 11, no. 11, 2025.

Copyright

Copyright © 2026 Ann Maria E C, Karthik K Krishna. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.