Embedding Earth: Self-supervised contrastive pre-training for dense land cover classification

• URL: http://arxiv.org/abs/2203.06041v1
• Date: Fri, 11 Mar 2022 16:14:14 GMT
• Title: Embedding Earth: Self-supervised contrastive pre-training for dense land cover classification
• Authors: Michail Tarasiou, Stefanos Zafeiriou
• Abstract summary: We present Embedding Earth a self-supervised contrastive pre-training method for leveraging the large availability of satellite imagery. We observe significant improvements up to 25% absolute mIoU when pre-trained with our proposed method. We find that learnt features can generalize between disparate regions opening up the possibility of using the proposed pre-training scheme.
• Score: 61.44538721707377
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In training machine learning models for land cover semantic segmentation there is a stark contrast between the availability of satellite imagery to be used as inputs and ground truth data to enable supervised learning. While thousands of new satellite images become freely available on a daily basis, getting ground truth data is still very challenging, time consuming and costly. In this paper we present Embedding Earth a self-supervised contrastive pre-training method for leveraging the large availability of satellite imagery to improve performance on downstream dense land cover classification tasks. Performing an extensive experimental evaluation spanning four countries and two continents we use models pre-trained with our proposed method as initialization points for supervised land cover semantic segmentation and observe significant improvements up to 25% absolute mIoU. In every case tested we outperform random initialization, especially so when ground truth data are scarse. Through a series of ablation studies we explore the qualities of the proposed approach and find that learnt features can generalize between disparate regions opening up the possibility of using the proposed pre-training scheme as a replacement to random initialization for Earth observation tasks. Code will be uploaded soon at https://github.com/michaeltrs/DeepSatModels.

Related papers

• Federated Adversarial Learning for Robust Autonomous Landing Runway Detection [6.029462194041386]
  In this paper, we propose a federated adversarial learning-based framework to detect landing runways. To the best of our knowledge, this marks the first instance of federated learning work that address the adversarial sample problem in landing runway detection.
  arXiv  Detail & Related papers  (2024-06-22T19:31:52Z)
• SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation [69.42764583465508]
  We explore the potential of generative image diffusion to address the scarcity of annotated data in earth observation tasks. To the best of our knowledge, we are the first to generate both images and corresponding masks for satellite segmentation.
  arXiv  Detail & Related papers  (2024-03-25T10:30:22Z)
• A Self-Supervised Approach to Land Cover Segmentation [1.0878040851638]
  Land use/land cover change (LULC) maps are integral resources in earth science and agricultural research. Due to the nature of such maps, the creation of LULC maps is often constrained by the time and human resources necessary to accurately annotate satellite imagery and remote sensing data. Here, we demonstrate a self-supervised method of land cover segmentation that has no need for high-quality ground truth labels.
  arXiv  Detail & Related papers  (2023-10-27T16:37:36Z)
• Evaluating the Label Efficiency of Contrastive Self-Supervised Learning for Multi-Resolution Satellite Imagery [0.0]
  Self-supervised learning has been applied in the remote sensing domain to exploit readily-available unlabeled data. In this paper, we study self-supervised visual representation learning through the lens of label efficiency.
  arXiv  Detail & Related papers  (2022-10-13T06:54:13Z)
• Self-supervised Audiovisual Representation Learning for Remote Sensing Data [96.23611272637943]
  We propose a self-supervised approach for pre-training deep neural networks in remote sensing. By exploiting the correspondence between geo-tagged audio recordings and remote sensing, this is done in a completely label-free manner. We show that our approach outperforms existing pre-training strategies for remote sensing imagery.
  arXiv  Detail & Related papers  (2021-08-02T07:50:50Z)
• DeepSatData: Building large scale datasets of satellite images for training machine learning models [77.17638664503215]
  This report presents design considerations for automatically generating satellite imagery datasets for training machine learning models. We discuss issues faced from the point of view of deep neural network training and evaluation.
  arXiv  Detail & Related papers  (2021-04-28T15:13:12Z)
• Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data [64.40187171234838]
  Seasonal Contrast (SeCo) is an effective pipeline to leverage unlabeled data for in-domain pre-training of re-mote sensing representations. SeCo will be made public to facilitate transfer learning and enable rapid progress in re-mote sensing applications.
  arXiv  Detail & Related papers  (2021-03-30T18:26:39Z)
• An Efficient Method for the Classification of Croplands in Scarce-Label Regions [0.0]
  Two of the main challenges for cropland classification by satellite time-series images are insufficient ground-truth data and inaccessibility of high-quality hyperspectral images for under-developed areas. Unlabeled medium-resolution satellite images are abundant, but how to benefit from them is an open question. We will show how to leverage their potential for cropland classification using self-supervised tasks.
  arXiv  Detail & Related papers  (2021-03-17T12:10:11Z)
• Geography-Aware Self-Supervised Learning [79.4009241781968]
  We show that due to their different characteristics, a non-trivial gap persists between contrastive and supervised learning on standard benchmarks. We propose novel training methods that exploit the spatially aligned structure of remote sensing data. Our experiments show that our proposed method closes the gap between contrastive and supervised learning on image classification, object detection and semantic segmentation for remote sensing.
  arXiv  Detail & Related papers  (2020-11-19T17:29:13Z)
• Land Cover Semantic Segmentation Using ResUNet [0.0]
  We present our work on developing an automated system for land cover classification. This system takes a multiband satellite image of an area as input and outputs the land cover map of the area at the same resolution as the input. For this purpose convolutional machine learning models were trained in the task of predicting the land cover semantic segmentation of satellite images.
  arXiv  Detail & Related papers  (2020-10-13T10:56:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.