Glosarium AI
Kamus lengkap Kecerdasan Buatan
Geospatial Instance Segmentation
Advanced computer vision technique that individually identifies and delineates each geographic object in a satellite or aerial image, enabling precise distinction between similar entities.
Satellite Object Detection
Automated process of locating and classifying specific elements (buildings, vehicles, vegetation) in high-resolution satellite images using deep learning algorithms.
Semantic Pixel Classification
Automatic assignment of a semantic category to each pixel of a geospatial image, creating a detailed thematic map of different land cover classes.
Aerial Image Analysis
Systematic extraction of relevant information from aerial photographs to identify, measure, and characterize ground structures and geographic phenomena.
Automatic Parcel Delineation
Algorithmic process that automatically identifies the boundaries of agricultural or land parcels by analyzing visual patterns and discontinuities in spatial imagery.
Geospatial Panoptic Segmentation
Unified approach combining semantic segmentation and instance segmentation to provide a complete and detailed understanding of the geospatial scene at pixel and object level.
Temporal Change Detection
Automatic identification of spatial and thematic modifications between multiple successive image acquisitions of the same geographic area to track landscape evolution.
Automatic Raster Vectorization
Algorithmic conversion of raster data (images) into vector structures (polygons, lines, points) to geometrically represent objects identified in spatial imagery.
Geometric primitive extraction
Automatic detection and characterization of fundamental geometric elements (points, lines, contours, shapes) in geospatial images for structured territory reconstruction.
Spatial anomaly detection
Identification of unusual patterns or objects in geospatial data that deviate significantly from the expected normal behavior in a given spatial context.
Supervised image segmentation
Machine learning approach using labeled training data to develop segmentation models capable of precisely classifying pixels according to predefined categories.
Spatial morphological analysis
Application of mathematical morphological operators to analyze and modify the spatial structure of objects in geospatial images, facilitating their segmentation and characterization.
Multi-scale object detection
Detection technique that operates simultaneously at multiple spatial resolutions to identify objects of varying sizes in geospatial images, from small elements to vast structures.
Object-based classification
Classification method that first groups pixels into homogeneous segments (objects) before classifying these segments rather than individual pixels, improving the spatial coherence of results.
Connected region segmentation
Algorithm that identifies continuous zones of similar pixels based on connectivity and spectral homogeneity criteria to delineate natural geospatial objects.
Geospatial edge detection
Automatic identification of discontinuity lines in spatial images corresponding to boundaries between different geographic entities (roads, rivers, administrative borders).
Spatial texture analysis
Evaluation of intensity variation patterns and spatial structure in geospatial imagery to discriminate between different surfaces and materials based on their textural characteristics.
Deep learning-based segmentation
Use of deep convolutional neural networks (U-Net, DeepLab, Mask R-CNN) to perform precise and automatic segmentation of geospatial imagery into distinct semantic objects.
Geospatial infrastructure detection
Automatic identification of anthropogenic infrastructure elements (road networks, buildings, power lines) in spatial imagery for mapping and urban planning.
Spatial feature extraction
Computational process that identifies and quantifies discriminating spatial attributes (shape, size, orientation, texture) of geospatial objects to facilitate their classification and analysis.