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Large-Scale Semantic Classification: Outcome of the First Year of Inria Aerial Image Labeling Benchmark
2018
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
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In this paper, we discuss the outcomes of the first year of the Inria benchmark contest. We first briefly recall the composition of the dataset and give its use statistics. ...
Finally, we conclude about the state-of-the-art in large-scale semantic labeling. ...
doi:10.1109/igarss.2018.8518525
dblp:conf/igarss/HuangLAKTMBSCBL18
fatcat:r5qsistsojb2zgreftjdbq7ldq
SatImNet: Structured and Harmonised Training Data for Enhanced Satellite Imagery Classification
[article]
2020
arXiv
pre-print
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This prevents the combination of two or more training datasets for improving image classification tasks based on machine learning. ...
Automatic supervised classification of satellite images with complex modelling such as deep neural networks requires the availability of representative training datasets. ...
Acknowledgement: The authors would like to thank Tomáš Kliment, Panagiotis Mavrogiorgos and Pier Valerio Tognoli for their contribution in data management and Docker images configuration and maintenance ...
arXiv:2006.10623v1
fatcat:g55uuyxgm5cpjccgc62u4xnyyi
A REVIEW OF BENCHMARKING IN PHOTOGRAMMETRY AND REMOTE SENSING
2019
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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The first part of the paper is a bibliographic analysis based on queries in Scopus and Web of Science databases which shows an increase in research activities based on benchmarking data. ...
As hereafter reported, benchmarking has increased in recent years, with many benchmarks being presented in the photogrammetric and remote sensing communities.</p> ...
Large-scale semantic classification: Outcome of the first year of inria aerial image labeling benchmark 2018 International Geoscience and Remote Sensing Symposium (IGARSS) Helber P., Bischke B., Dengel ...
doi:10.5194/isprs-archives-xlii-1-w2-1-2019
fatcat:f26tkpwl6rddlihiq3tvfjbkam
DISIR: Deep Image Segmentation with Interactive Refinement
[article]
2020
arXiv
pre-print
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This paper presents an interactive approach for multi-class segmentation of aerial images. Precisely, it is based on a deep neural network which exploits both RGB images and annotations. ...
Starting from an initial output based on the image only, our network then interactively refines this segmentation map using a concatenation of the image and user annotations. ...
The INRIA Aerial Image Labelling dataset (Maggiori et al., 2017) is composed of two classes (buildings and not buildings) and covers more than 800 km 2 with a spatial resolution of 0.3m. ...
arXiv:2003.14200v1
fatcat:6d3hhib5xvavzj5ykreg7alx6e
DISIR: DEEP IMAGE SEGMENTATION WITH INTERACTIVE REFINEMENT
2020
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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This paper presents an interactive approach for multi-class segmentation of aerial images. Precisely, it is based on a deep neural network which exploits both RGB images and annotations. ...
Starting from an initial output based on the image only, our network then interactively refines this segmentation map using a concatenation of the image and user annotations. ...
., 2019) assess these various strategies in the first large scale study of interactive instance segmentation with human annotators. ...
doi:10.5194/isprs-annals-v-2-2020-877-2020
fatcat:vn6drv76mnhhphjq2hvvb6w42m
Land-use Mapping for High Spatial Resolution Remote Sensing Image via Deep Learning: A Review
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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We then briefly review the fundamentals and the developments of the development of semantic segmentation and single object segmentation. ...
With the emergence of new deep learning (DL) algorithms in recent years, methods to LUM with DL have achieved huge breakthroughs, which offers novel opportunities for the development of LUM for HSR-RSIs ...
Buffalo dataset [39] is composed of 30 aerial images of Buffalo city at a resolution of 1 meter and all with a size of 609 × 914. Inria Aerial Image Labeling dataset [40] covers 810 Km 2 . ...
doi:10.1109/jstars.2021.3078631
fatcat:ubblvynhdvd2ndzhjablus22cy
AUTOMATED CHAIN FOR LARGE-SCALE 3D RECONSTRUCTION OF URBAN SCENES FROM SATELLITE IMAGES
2019
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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In this paper, we present an automated chain for large-scale 3D reconstruction of urban scenes with a Level of Detail 1 from satellite images. The proposed framework relies on two key ingredient. ...
First, from a stereo pair of images, we estimate a digital terrain model and a digital height model, by using a novel set of feature descriptors based on multiscale morphological analysis. ...
Inspired by the winning method of the Inria aerial image labeling benchmark (Huang et al., 2018) , we have adopted the original U-Net architecture from (Ronneberger et al., 2015) , with a single major ...
doi:10.5194/isprs-archives-xlii-2-w16-243-2019
fatcat:l634kjzeozcqrpiuxwwrz73bwm
Research Contribution and Comprehensive Review towards the Semantic Segmentation of Aerial Images Using Deep Learning Techniques
2022
Security and Communication Networks
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The main aim of this review is to provide a clear algorithmic categorization and analysis of the diverse contribution of semantic segmentation of aerial images and expects to give the comprehensive details ...
In recent years, semantic segmentation has been focused on different deep learning approaches in the area of computer vision, which has aimed for getting superior efficiency while analyzing the aerial ...
Acknowledgments e authors acknowledge the help of the Vellore Institute of Technology, Vellore, India, for giving excellent assets to this work. ...
doi:10.1155/2022/6010912
fatcat:qxoogfb3zneypkh5w3m5p3ts3e
Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study
[article]
2020
arXiv
pre-print
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We introduce a novel large-scale dataset for semi-supervised semantic segmentation in Earth Observation, the MiniFrance suite. ...
MiniFrance has several unprecedented properties: it is large-scale, containing over 2000 very high resolution aerial images, accounting for more than 200 billions samples (pixels); it is varied, covering ...
The authors acknowledge the IGN for providing the BD ORTHO database under Open Licence v1.0 (https://www.etalab.gouv.fr/ licence-ouverte-open-licence) and the European Copernicus Program for providing ...
arXiv:2010.07830v1
fatcat:h7k5dnh5nnhffl2iy4o6yg67du
Automatic Building Extraction on Satellite Images Using Unet and ResNet50
2022
Computational Intelligence and Neuroscience
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This study proposes the differentiation of buildings by image segmentation on high-resolution satellite images with U-net architecture. ...
It was aimed to remove buildings in the high-density city of Boston. In the U-net architecture, image segmentation is performed with different encoders and the results are compared. ...
aerial image labeling dataset[45]. ...
doi:10.1155/2022/5008854
pmid:35222630
pmcid:PMC8881177
fatcat:3jck52yfgrcg5jxuwiicsjy3yu
Arbitrary-Shaped Building Boundary-Aware Detection with Pixel Aggregation Network
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Large-scale building extraction is an essential work in the field of remote sensing image analysis. ...
The high-resolution image extraction methods based on deep learning have achieved state-of-the-art performance. ...
Maggiori et al. for providing the Inria Aerial Image Labeling Benchmark, and grateful to Volodymyr Mnih for providing the Massachusetts buildings dataset. We are also grateful to F. ...
doi:10.1109/jstars.2020.3017934
fatcat:cwjmgnvpkjeujalhy7vwniekre
Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends
2020
Remote Sensing
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Deep learning (DL) has great influence on large parts of science and increasingly established itself as an adaptive method for new challenges in the field of Earth observation (EO). ...
To lower the barriers for researchers in EO, this review gives an overview of the evolution of DL with a focus on image segmentation and object detection in convolutional neural networks (CNN). ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/rs12101667
fatcat:vlqupucfrndexnyhrsgawshc2y
A Systematic Survey of ML Datasets for Prime CV Research Areas—Media and Metadata
2021
Data
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The ever-growing capabilities of computers have enabled pursuing Computer Vision through Machine Learning (i.e., MLCV). ML tools require large amounts of information to learn from (ML datasets). ...
We reveal the heterogeneous plethora that comprises the MLCV dataset tissue; their continuous growth in volume and complexity; the specificities of the evolution of their media and metadata components ...
Those images have large variations in scale, pose, and lighting. ...
doi:10.3390/data6020012
fatcat:jov2btfknnet3bwpyn6txnrhda
Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications
2020
Remote Sensing
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In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by investigating aggregated classes. ...
In this review, we provide a comprehensive overview of the impact of deep learning on EO applications by reviewing 429 studies on image segmentation and object detection with CNNs. ...
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A. ...
doi:10.3390/rs12183053
doaj:56aade2b0b4243b1b45a6839cc85dc15
fatcat:jskmiupd4zaa5egsllib2oyioi
Synthetic Data for Deep Learning
[article]
2019
arXiv
pre-print
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First, we discuss synthetic datasets for basic computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., semantic segmentation), synthetic environments and datasets ...
In this work, we attempt to provide a comprehensive survey of the various directions in the development and application of synthetic data. ...
opinion, define state of the art in the field for years to come. ...
arXiv:1909.11512v1
fatcat:qquxnw4dfvgmfeztbpdqhr44gy
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