Reuse out-of-year data to enhance land cover mappingvia feature disentanglement and contrastive learning

• URL: http://arxiv.org/abs/2404.11114v1
• Date: Wed, 17 Apr 2024 07:00:20 GMT
• Title: Reuse out-of-year data to enhance land cover mappingvia feature disentanglement and contrastive learning
• Authors: Cassio F. Dantas, Raffaele Gaetano, Claudia Paris, Dino Ienco,
• Abstract summary: Land use/land cover (LULC) maps play a pivotal role in supporting agricultural territory management, environmental monitoring and sustainable decision-making. New ground truth data must be collected, leading to the complete disregard of previously gathered reference data. We propose a deep learning framework to combine remote sensing and reference data coming from two different domains.
• Score: 5.936030178022172
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Timely up-to-date land use/land cover (LULC) maps play a pivotal role in supporting agricultural territory management, environmental monitoring and facilitating well-informed and sustainable decision-making. Typically, when creating a land cover (LC) map, precise ground truth data is collected through time-consuming and expensive field campaigns. This data is then utilized in conjunction with satellite image time series (SITS) through advanced machine learning algorithms to get the final map. Unfortunately, each time this process is repeated (e.g., annually over a region to estimate agricultural production or potential biodiversity loss), new ground truth data must be collected, leading to the complete disregard of previously gathered reference data despite the substantial financial and time investment they have required. How to make value of historical data, from the same or similar study sites, to enhance the current LULC mapping process constitutes a significant challenge that could enable the financial and human-resource efforts invested in previous data campaigns to be valued again. Aiming to tackle this important challenge, we here propose a deep learning framework based on recent advances in domain adaptation and generalization to combine remote sensing and reference data coming from two different domains (e.g. historical data and fresh ones) to ameliorate the current LC mapping process. Our approach, namely REFeD (data Reuse with Effective Feature Disentanglement for land cover mapping), leverages a disentanglement strategy, based on contrastive learning, where invariant and specific per-domain features are derived to recover the intrinsic information related to the downstream LC mapping task and alleviate possible distribution shifts between domains. Additionally, REFeD is equipped with an effective supervision scheme where feature disentanglement is further enforced via multiple levels of supervision at different granularities. The experimental assessment over two study areas covering extremely diverse and contrasted landscapes, namely Koumbia (located in the West-Africa region, in Burkina Faso) and Centre Val de Loire (located in centre Europe, France), underlines the quality of our framework and the obtained findings demonstrate that out-of-year information coming from the same (or similar) study site, at different periods of time, can constitute a valuable additional source of information to enhance the LC mapping process.

Related papers

• Biological Valuation Map of Flanders: A Sentinel-2 Imagery Analysis [12.025312586542318]
  We present a densely labeled ground truth map of Flanders paired with Sentinel-2 satellite imagery. Our methodology includes a formalized dataset division and sampling method, utilizing the topographic map layout 'Kaartbladversnijdingen,' and a detailed semantic segmentation model training pipeline.
  arXiv  Detail & Related papers  (2024-01-26T22:21:39Z)
• Cross Domain Early Crop Mapping using CropSTGAN [12.271756709807898]
  This paper introduces the Crop Mapping Spectral-temporal Generative Adrial Neural Network (CropSTGAN) CropSTGAN learns to transform the target domain's spectral features to those of the source domain, effectively bridging large dissimilarities. In experiments, CropSTGAN is benchmarked against various state-of-the-art (SOTA) methods.
  arXiv  Detail & Related papers  (2024-01-15T00:27:41Z)
• A Comprehensive Survey on Source-free Domain Adaptation [69.17622123344327]
  The research of Source-Free Domain Adaptation (SFDA) has drawn growing attention in recent years. We provide a comprehensive survey of recent advances in SFDA and organize them into a unified categorization scheme. We compare the results of more than 30 representative SFDA methods on three popular classification benchmarks.
  arXiv  Detail & Related papers  (2023-02-23T06:32:09Z)
• Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning [122.62311703151215]
  Divide and Contrast (DaC) aims to connect the good ends of both worlds while bypassing their limitations. DaC divides the target data into source-like and target-specific samples, where either group of samples is treated with tailored goals. We further align the source-like domain with the target-specific samples using a memory bank-based Maximum Mean Discrepancy (MMD) loss to reduce the distribution mismatch.
  arXiv  Detail & Related papers  (2022-11-12T09:21:49Z)
• 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)
• Tackling Long-Tailed Category Distribution Under Domain Shifts [50.21255304847395]
  Existing approaches cannot handle the scenario where both issues exist. We designed three novel core functional blocks including Distribution Calibrated Classification Loss, Visual-Semantic Mapping and Semantic-Similarity Guided Augmentation. Two new datasets were proposed for this problem, named AWA2-LTS and ImageNet-LTS.
  arXiv  Detail & Related papers  (2022-07-20T19:07:46Z)
• Jalisco's multiclass land cover analysis and classification using a novel lightweight convnet with real-world multispectral and relief data [51.715517570634994]
  We present our novel lightweight (only 89k parameters) Convolution Neural Network (ConvNet) to make LC classification and analysis. In this work, we combine three real-world open data sources to obtain 13 channels. Our embedded analysis anticipates the limited performance in some classes and gives us the opportunity to group the most similar.
  arXiv  Detail & Related papers  (2022-01-26T14:58:51Z)
• Domain-Adversarial Training of Self-Attention Based Networks for Land Cover Classification using Multi-temporal Sentinel-2 Satellite Imagery [0.0]
  Most practical applications cannot rely on labeled data, and in the field, surveys are a time consuming solution. In this paper, we investigate adversarial training of deep neural networks to bridge the domain discrepancy between distinct geographical zones.
  arXiv  Detail & Related papers  (2021-04-01T15:45:17Z)
• Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks [77.63950365605845]
  Farm parcel delineation provides cadastral data that is important in developing and managing climate change policies. This data can also be useful for the agricultural insurance sector for assessing compensations following damages associated with extreme weather events. Using satellite imaging can be a scalable and cost effective manner to perform the task of farm parcel delineation.
  arXiv  Detail & Related papers  (2020-04-11T19:49:09Z)
• Fine grained classification for multi-source land cover mapping [4.9873153106566575]
  Timely and accurate land use/land cover mapping can support this vision. Deep learning approach is proposed to deal with multi-source land cover mapping at object level.
  arXiv  Detail & Related papers  (2020-04-04T15:49:37Z)

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.