A benchmark GaoFen-7 dataset for building extraction from satellite images DOI Creative Commons
Peimin Chen, Huabing Huang,

Feng Ye

et al.

Scientific Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: Feb. 10, 2024

Abstract Accurate building extraction is crucial for urban understanding, but it often requires a substantial number of samples. While some datasets are available model training, there remains lack high-quality covering and rural areas in China. To fill this gap, study creates high-resolution GaoFen-7 (GF-7) Building dataset utilizing the Chinese GF-7 imagery from six cities. The comprises 5,175 pairs 512 × image tiles, 573.17 km 2 . It contains 170,015 buildings, with 84.8% buildings 15.2% areas. usability has been proved seven convolutional neural networks, all achieving an overall accuracy (OA) exceeding 93%. Experiments have shown that can be used scenarios. proposed boasts high quality diversity. supplements existing will contribute to promoting new algorithms extraction, as well facilitating intelligent interpretation

Language: Английский

SIDEST: A sample-free framework for crop field boundary delineation by integrating super-resolution image reconstruction and dual edge-corrected Segment Anything model DOI
Haoran Sun,

Zhijian Wei,

Weiguo Yu

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 230, P. 109897 - 109897

Published: Jan. 10, 2025

Language: Английский

Citations

1
Extracting vectorized agricultural parcels from high-resolution satellite images using a Point-Line-Region interactive multitask model DOI
Mengmeng Li,

Chengwen Lu,

Mengjing Lin

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 231, P. 109953 - 109953

Published: Jan. 22, 2025

Language: Английский

Citations

1
Comparative Study of Agricultural Parcel Delineation Deep Learning Methods using Satellite Images: Validation through Parcels Complexity DOI Creative Commons
Amine Hadir, Mohamed Adjou,

Olga Assainova

et al.

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100833 - 100833

Published: Feb. 1, 2025

Language: Английский

Citations

1
Enhancing Crop Mapping through Automated Sample Generation Based on Segment Anything Model with Medium-Resolution Satellite Imagery DOI Creative Commons
Jialin Sun, Shuai Yan, Thomas Alexandridis

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(9), P. 1505 - 1505

Published: April 24, 2024

Crop mapping using satellite imagery is crucial for agriculture applications. However, a fundamental challenge that hinders crop progress the scarcity of samples. The latest foundation model, Segment Anything Model (SAM), provides an opportunity to address this issue, yet few studies have been conducted in area. This study investigated parcel segmentation performance SAM on commonly used medium-resolution (i.e., Sentinel-2 and Landsat-8) proposed novel automated sample generation framework based SAM. comprises three steps. First, image optimization automatically selects high-quality images as inputs Then, potential samples are generated masks produced by Finally, subsequently subjected cleaning procedure acquire most reliable Experiments were Henan Province, China, southern Ontario, Canada, six proven effective classifiers. effectiveness our method demonstrated through combination field-survey-collected differently proportioned Our results indicated directly remains challenging, unless parcels large, regular shape, distinct color differences from surroundings. Additionally, approach significantly improved classifiers alleviated problem. Compared trained only samples, resulted average improvement 16% 78.5% respectively. random forest achieved relatively good performance, with weighted-average F1 0.97 0.996 obtained two areas, contributes insights into solutions highlights promising application models like

Language: Английский

Citations

8
A novel weakly-supervised method based on the segment anything model for seamless transition from classification to segmentation: A case study in segmenting latent photovoltaic locations DOI Creative Commons
Ruiqing Yang, Guojin He, Ranyu Yin

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 130, P. 103929 - 103929

Published: May 25, 2024

In the quest for large-scale photovoltaic (PV) panel extraction, substantial data volumes are essential, given demand sub-meter rooftop PV resolution. This requires concept of Latent Photovoltaic Locations (LPL) to reduce scope amount subsequent processing. order minimize manual annotation, a pioneering weakly-supervised framework is proposed, which capable generating pixel-level annotations segmentation based on image-level and provides two datasets required classification-then-segmentation strategy without more annotations. The strong noise-resistance Segment Anything Model (SAM) discovered in extremely difficult rough coarse pseudo-label refinement, which, after integrating probability updating mechanism, achieves seamless transition from scene classification semantic segmentation. resulting national LPL distribution map, rendered at 2 m resolution, showcases commendable 92 % accuracy F1-score 91 %, advantages terms efficiency have been verified through large number experiments. process explores how use fundamental models accelerate remote sensing information extraction process, crucial current trajectory deep learning sensing. relevant code available https://github.com/Github-YRQ/LPL.

Language: Английский

Citations

8
Learning without Exact Guidance: Updating Large-Scale High-Resolution Land Cover Maps from Low-Resolution Historical Labels DOI
Zhuohong Li,

Wei He,

Jiepan Li

et al.

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Journal Year: 2024, Volume and Issue: 64, P. 27717 - 27727

Published: June 16, 2024

Language: Английский

Citations

8
FusionVision: A Comprehensive Approach of 3D Object Reconstruction and Segmentation from RGB-D Cameras Using YOLO and Fast Segment Anything DOI Creative Commons
Safouane El Ghazouali,

Youssef Mhirit,

Ali Oukhrid

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(9), P. 2889 - 2889

Published: April 30, 2024

In the realm of computer vision, integration advanced techniques into pre-processing RGB-D camera inputs poses a significant challenge, given inherent complexities arising from diverse environmental conditions and varying object appearances. Therefore, this paper introduces FusionVision, an exhaustive pipeline adapted for robust 3D segmentation objects in imagery. Traditional vision systems face limitations simultaneously capturing precise boundaries achieving high-precision detection on depth maps, as they are mainly proposed RGB cameras. To address FusionVision adopts integrated approach by merging state-of-the-art techniques, with instance methods. The these components enables holistic (unified analysis information obtained both color D channels) interpretation data, facilitating extraction comprehensive accurate order to improve post-processes such 6D pose estimation, Simultanious Localization Mapping (SLAM) operations, dataset extraction, etc. employs YOLO identifying within image domain. Subsequently, FastSAM, innovative semantic model, is applied delineate boundaries, yielding refined masks. synergy between their scene understanding ensures cohesive fusion segmentation, enhancing overall precision segmentation.

Language: Английский

Citations

7
MeSAM: Multiscale Enhanced Segment Anything Model for Optical Remote Sensing Images DOI
Xichuan Zhou, Fu Liang, Lihui Chen

et al.

IEEE Transactions on Geoscience and Remote Sensing, Journal Year: 2024, Volume and Issue: 62, P. 1 - 15

Published: Jan. 1, 2024

Segment anything model (SAM) has been widely applied to various downstream tasks for its excellent performance and generalization capability. However, SAM exhibits three limitations related remote sensing semantic segmentation task: 1) the image encoders excessively lose high-frequency information, such as object boundaries textures, resulting in rough masks; 2) due being trained on natural images, faces difficulty accurately recognizing objects with large-scale variations uneven distribution images; 3) output tokens used mask prediction are images not applicable segmentation. In this paper, we explore an efficient paradigm applying of images. Furthermore, propose MeSAM, a new fine-tuning method more suitable adapt it tasks. Our first introduces inception mixer into encoder effectively preserve features. Secondly, by designing decoder remote-sensing correction incorporating multiscale connections, make up difference from Experimental results demonstrated that our significantly improves accuracy outperforming some state-of-the-art methods. The code will be available at https://github.com/Magic-lem/MeSAM.

Language: Английский

Citations

7
A Segment Anything Model based weakly supervised learning method for crop mapping using Sentinel-2 time series images DOI Creative Commons
Jialin Sun, Shuai Yan, Xiaochuang Yao

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 133, P. 104085 - 104085

Published: Aug. 10, 2024

Language: Английский

Citations

7
Enhancing USDA NASS Cropland Data Layer with Segment Anything Model DOI
Chen Zhang,

Purva Marfatia,

Hamza Farhan

et al.

Published: July 25, 2023

Crop-specific land cover mapping is a vital application in agro-geoinformatics with the proliferation of remote sensing data and machine learning techniques. This paper presents novel approach to enhance well-known Cropland Data Layer (CDL) product by U.S. Department Agriculture (USDA) National Agricultural Statistics Service (NASS) using Meta's Segment Anything Model (SAM). The study leverages SAM's zero-shot generalization capability automatically delineate cropland fields from Sentinel-2 images. By voting for major crop types within each delineated unit, substantial number noisy pixels CDL can be eliminated, leading notable improvements accuracy. Preliminary experimental results across key agricultural regions U.S., such as California's Central Valley Corn Belt, suggest that SAM significantly quality original data. ability refine crop-specific data, like CDL, demonstrates practical applicability monitoring systems. Moreover, result showcases promising potential integrating into existing type classification workflows create high-quality early- in-season maps on national scale minimal effort.

Language: Английский

Citations

15