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Segmentation is crucial in geographic object-based image analysis (GEOBIA) for accurate land use and land cover (LULC) mapping. However, obtaining outstanding segmentation results in all scenarios proves challenging with a single algorithm. This study investigates seven segmentation algorithms: MF, SPRING, ESP2 (three global-scale algorithms), IODA, SA, EIODA (three local-scale optimization algorithms) and Segment Anything Model (SAM) (deep learning). In the custom dataset and semantic segmentation datasets, we apply visual interpretation, unsupervised, and supervised evaluation methods with 15 test images, using a total of 17 evaluation indices to assess the segmentation results. Based on the evaluation results, the effectiveness and adaptability of the algorithms in scene segmentation are comprehensively analyzed. The results report that global-scale segmentation approaches encounter difficulties in distinguishing meaningful objects in complicated scenarios. Both MF and SPRING methods are prone to over-segmentation. In many cases, ESP2 tends to generate homogeneous segments (low weighted variance (WV)), while EIODA tends to produce heterogeneous adjacent segments (low Moran&#x0027;s I (MI)). ED3 and segmentation evaluation index (SEI) demonstrate that SA and IODA can to some extent identify geo-objects, with SA being more effective and performing exceptionally well in building extraction. The EIODA performs well in areas with clear boundaries, like aquaculture ponds and water-land transitions. SAM accurately detects objects of various sizes, displaying rich semantic content and high consistency with reference polygons. The average intersection over union (IoU) reaches 71.10&#x0025; and F measure attains 0.77 under normal conditions.

Evaluation of Global-Scale and Local-Scale Optimized Segmentation Algorithms in GEOBIA with SAM on Land Use and Land Cover