## 4 Discussion  
The segmentation of PlanetScope data for the purpose of enhancing LPIS updates and maintenance has demonstrated significant potential. The stack of twenty-five NDVI images with a geometric resolution of 3 m and a dense temporal component provided valuable data for land-parcel delineation. A key finding of this study is that the edge-based segmentation method, when applied to multitemporal PlanetScope imagery, effectively extracts parcel boundaries with an acceptable level of accuracy, despite the discrepancy in geometric resolution between PlanetScope data (3 m) and the traditional aerial orthophotos employed by AGEA (20 cm) for the same purpose. These findings align with the results of previous studies demonstrating the utility of multitemporal optical data for agricultural monitoring [10; 11; 12]. The geometric, supervised method employed to assess the segmentation accuracy highlights that when the PlanetScope multitemporal NDVI dataset was segmented into four different scale levels (10%, 20%, 30%, 40%), the mean weighted accuracy rates ranged from 79.33% to 83.33%, with the highest accuracy recorded at a 30% scale level. This evaluation method takes into account the areal relationship between reference and segmented parcel areas. Although this approach has been widely discussed in the literature [23; 27], this study enhances segmentation evaluation by integrating the two key metrics--overflowing area rate and overlapping area rate--that influence segmentation accuracy in opposite ways, along with the sign of the area difference. This integration enables the identification of error types in cases of low accuracy, which is essential for recalibrating the segmentation algorithm and enhancing its overall performance. The major strength of this approach is the incorporation of temporal resolution as a crucial factor for parcel delineation. The results suggest that the availability of the temporal dimension can compensate for spatial resolutions lower than those of traditional orthophotos. The incorporation of the temporal path of vegetation indices enables the consideration of the phenological cycles of vegetation coverage within the parcels, enabling  
Figure 13: Detail of parcel 90, classified as Pastures, illustrating the variability of land cover in this class. The reference parcel (outlined in aqua green) contains a mixture of grasslands (central and upper sides), bare soil (lower-right corner) and trees (right-side of segmented parcel). Consequently, the corresponding segmented parcel was extracted with low accuracy, exhibiting a shape significantly smaller than the reference. a more comprehensive and reliable delineation of their boundaries. This appears to fully justify a shift from a static LPIS paradigm, which is based on mono-temporal data, to a dynamic LPIS paradigm based on multi-temporal analysis. A further strength of the survey lies in investigating segmentation and refining quantitative methodologies for assessing segmentation accuracy. Indeed, such assessment is commonly undertaken by means of qualitative methods or regarded as a preliminary step in object-based image analysis (OBIA). However, frequently, these types of analyses only assess the accuracy of segment classification, rather than evaluate the segmentation step itself [17]. A third strength of the study is the comparative approach across different scales, which enables a critical examination of the segmentation algorithm, improving our understanding of its functioning and its impact on result quality. A primary limit of the research pertains to the prevailing reliance on visual interpretation and manual intervention for the establishment of the one-to-one correspondence between segmented and reference parcels. The full automation of this task is particularly challenging due to the difficulty of parametrizing the wide variety of conditions required to accurately determine which segment best corresponds to the reference. Another limitation is the adoption of only a geometric method to evaluate segmentation accuracy.