Nothing Special   »   [go: up one dir, main page]

Lu et al., 2024 - Google Patents

DCV2I: A Practical Approach for Supporting Geographers' Visual Interpretation in Dune Segmentation with Deep Vision Models

Lu et al., 2024

View PDF
Document ID
17130450074596765936
Author
Lu A
Wu Z
Jiang Z
Wang W
Hasi E
Wang Y
Publication year
Publication venue
Proceedings of the AAAI Conference on Artificial Intelligence

External Links

Snippet

Visual interpretation is extremely important in human geography as the primary technique for geographers to use photograph data in identifying, classifying, and quantifying geographic and topological objects or regions. However, it is also time-consuming and …
Continue reading at ojs.aaai.org (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30244Information retrieval; Database structures therefor; File system structures therefor in image databases
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/20Image acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10032Satellite or aerial image; Remote sensing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
Pi et al. Detection and semantic segmentation of disaster damage in UAV footage
Gamanya et al. An automated satellite image classification design using object-oriented segmentation algorithms: A move towards standardization
Zhang et al. Natural language description of remote sensing images based on deep learning
WO2022218396A1 (en) Image processing method and apparatus, and computer readable storage medium
Li et al. Multi‐scale attention encoder for street‐to‐aerial image geo‐localization
Yu et al. Analysis of large-scale UAV images using a multi-scale hierarchical representation
CN113223042B (en) Intelligent acquisition method and equipment for remote sensing image deep learning sample
CN114418021B (en) Model optimization method, device and computer program product
Huang et al. Depth semantic segmentation of tobacco planting areas from unmanned aerial vehicle remote sensing images in plateau mountains
Du et al. Open-pit mine extraction from very high-resolution remote sensing images using OM-DeepLab
Yang et al. Lightweight Attention-Guided YOLO With Level Set Layer for Landslide Detection From Optical Satellite Images
Zhao et al. YOLO‐Highway: An Improved Highway Center Marking Detection Model for Unmanned Aerial Vehicle Autonomous Flight
Tian et al. ArGue: Attribute-Guided Prompt Tuning for Vision-Language Models
Li et al. Progressive attention-based feature recovery with scribble supervision for saliency detection in optical remote sensing image
Moghalles et al. Weakly supervised building semantic segmentation via superpixel‐CRF with initial deep seeds guiding
Zhang et al. A novel knowledge-driven automated solution for high-resolution cropland extraction by cross-scale sample transfer
Lu et al. DCV2I: A Practical Approach for Supporting Geographers’ Visual Interpretation in Dune Segmentation with Deep Vision Models
Khatua et al. Developing approaches in building classification and extraction with synergy of YOLOV8 and SAM models
CN109583371A (en) Landmark information based on deep learning extracts and matching process
Nir et al. CAST: Character labeling in Animation using Self‐supervision by Tracking
Ayazi et al. Comparison of traditional and machine learning base methods for ground point cloud labeling
Shi et al. Intelligent classification of land cover types in open-pit mine area using object-oriented method and multitask learning
Lu et al. DCV 2 I DCV^2I: Leveraging deep vision models to support geographers' visual interpretation in dune segmentation
Pan et al. Semi-Supervised Cross Domain Teacher-Student Mutual Training for Damaged Building Detection
Xiao et al. Foundation models for remote sensing and earth observation: A survey