Deep Learning for Satellite Image Time Series Analysis: A Review
- URL: http://arxiv.org/abs/2404.03936v2
- Date: Thu, 11 Apr 2024 13:02:58 GMT
- Title: Deep Learning for Satellite Image Time Series Analysis: A Review
- Authors: Lynn Miller, Charlotte Pelletier, Geoffrey I. Webb,
- Abstract summary: This review presents a summary of the state-of-the-art methods of modelling environmental, agricultural, and other Earth observation variables from SITS data using deep learning methods.
- Score: 5.882962965835289
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Earth observation (EO) satellite missions have been providing detailed images about the state of the Earth and its land cover for over 50 years. Long term missions, such as NASA's Landsat, Terra, and Aqua satellites, and more recently, the ESA's Sentinel missions, record images of the entire world every few days. Although single images provide point-in-time data, repeated images of the same area, or satellite image time series (SITS) provide information about the changing state of vegetation and land use. These SITS are useful for modeling dynamic processes and seasonal changes such as plant phenology. They have potential benefits for many aspects of land and natural resource management, including applications in agricultural, forest, water, and disaster management, urban planning, and mining. However, the resulting satellite image time series (SITS) are complex, incorporating information from the temporal, spatial, and spectral dimensions. Therefore, deep learning methods are often deployed as they can analyze these complex relationships. This review presents a summary of the state-of-the-art methods of modelling environmental, agricultural, and other Earth observation variables from SITS data using deep learning methods. We aim to provide a resource for remote sensing experts interested in using deep learning techniques to enhance Earth observation models with temporal information.
Related papers
- Advancing Earth Observation: A Survey on AI-Powered Image Processing in Satellites [0.0]
Advancements in technology have led to a substantial growth in the quality & quantity of imagery captured by Earth Observation (EO) satellites.
This has presented a challenge to the efficacy of the traditional workflow of transmitting this imagery to Earth for processing.
An approach to addressing this issue is to use pre-trained artificial intelligence models to process images on-board the satellite.
arXiv Detail & Related papers (2025-01-21T10:48:13Z) - EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision [72.84868704100595]
This paper presents a dataset specifically designed for self-supervision on remote sensing data, intended to enhance deep learning applications on Earth monitoring tasks.
The dataset spans 15 tera pixels of global remote-sensing data, combining imagery from a diverse range of sources, including NEON, Sentinel, and a novel release of 1m spatial resolution data from Satellogic.
Accompanying the dataset is EarthMAE, a tailored Masked Autoencoder developed to tackle the distinct challenges of remote sensing data.
arXiv Detail & Related papers (2025-01-14T13:42:22Z) - Rapid Automated Mapping of Clouds on Titan With Instance Segmentation [0.49478969093606673]
We apply a Mask R-CNN trained via transfer learning to perform instance segmentation of clouds in Titan images acquired by the Cassini spacecraft.
Despite Titan specific challenges, our approach yields accuracy comparable to contemporary cloud identification studies on Earth and other worlds.
We suggest that such approaches have broad potential for application to similar problems in planetary science where they are currently under-utilized.
arXiv Detail & Related papers (2025-01-08T12:30:06Z) - EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues [46.601134018876955]
EarthDial is a conversational assistant specifically designed for Earth Observation (EO) data.
It transforms complex, multi-sensory Earth observations into interactive, natural language dialogues.
EarthDial supports multi-spectral, multi-temporal, and multi-resolution imagery.
arXiv Detail & Related papers (2024-12-19T18:57:13Z) - Foundation Models for Remote Sensing and Earth Observation: A Survey [101.77425018347557]
This survey systematically reviews the emerging field of Remote Sensing Foundation Models (RSFMs)
It begins with an outline of their motivation and background, followed by an introduction of their foundational concepts.
We benchmark these models against publicly available datasets, discuss existing challenges, and propose future research directions.
arXiv Detail & Related papers (2024-10-22T01:08:21Z) - Deep Multimodal Fusion for Semantic Segmentation of Remote Sensing Earth Observation Data [0.08192907805418582]
This paper proposes a late fusion deep learning model (LF-DLM) for semantic segmentation.
One branch integrates detailed textures from aerial imagery captured by UNetFormer with a Multi-Axis Vision Transformer (ViT) backbone.
The other branch captures complex-temporal dynamics from the Sentinel-2 satellite imageMax time series using a U-ViNet with Temporal Attention (U-TAE)
arXiv Detail & Related papers (2024-10-01T07:50:37Z) - Semantic Segmentation of Vegetation in Remote Sensing Imagery Using Deep
Learning [77.34726150561087]
We propose an approach for creating a multi-modal and large-temporal dataset comprised of publicly available Remote Sensing data.
We use Convolutional Neural Networks (CNN) models that are capable of separating different classes of vegetation.
arXiv Detail & Related papers (2022-09-28T18:51:59Z) - Satellite Image Time Series Analysis for Big Earth Observation Data [50.591267188664666]
This paper describes sits, an open-source R package for satellite image time series analysis using machine learning.
We show that this approach produces high accuracy for land use and land cover maps through a case study in the Cerrado biome.
arXiv Detail & Related papers (2022-04-24T15:23:25Z) - Embedding Earth: Self-supervised contrastive pre-training for dense land
cover classification [61.44538721707377]
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.
arXiv Detail & Related papers (2022-03-11T16:14:14Z) - Deep Neural Networks for automatic extraction of features in time series
satellite images [3.3598755777055374]
We exploit both temporal and spatial information provided by Landsat Sentinel, SPOT, and other time series images to generate land cover maps.
Experimental results show that the Pleiades temporal information allows increasing the accuracy of land cover classification, thus producing up-to-date maps that can help in identifying changes on earth.
arXiv Detail & Related papers (2020-08-17T09:26:52Z) - Attentive Weakly Supervised land cover mapping for object-based
satellite image time series data with spatial interpretation [4.549831511476249]
We propose a new deep learning framework, named TASSEL, that is able to intelligently exploit the weak supervision provided by the coarse granularity labels.
Our framework also produces an additional side-information that supports the model interpretability with the aim to make the black box gray.
arXiv Detail & Related papers (2020-04-30T10:23:12Z)
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.