SEGMENTATION OF PARCEL BOUNDARY INDICATIONS IN VERY HIGH-RESOLUTION ORTHOPHOTO MOSAICS FOR CONTROL POINT IDENTIFICATION

Abstract

Several Land Parcel Geometry issues in Indonesia's Land Registration Process, such as parcel overlapping, gaps between parcels, and incorrect parcel shapes and sizes, are currently being addressed through a block adjustment approach. One crucial aspect of the block adjustment process is determining control points that tie the parcel geometry to the land coordinate system. Detailed Observations and measurements of parcel points in the field and aerial photographs established these control or tie points. Rectifying land parcels requires many control points, requiring substantial time and effort. The automation phase is critical to expedite the control point identification process. This research uses artificial intelligence techniques to identify control points in very high-resolution orthophoto mosaics. The method employed for control point identification involves the Segment Anything Model (SAM) algorithm to segment parcel boundary indications accurately. Enhance the quality of segmentation results conducted by fine-tuning, followed by centerline extraction and refinement of the extracted data. Based on the segmentation, a SAM model capable of accurately segmenting building objects is attained, After the centerline extraction process and modifications to the existing geometric operations within the GIS Tool, at the edges of buildings, fences, and walls derived points. These points can serve as control point indications in the block adjustment process.