Global High Categorical Resolution Land Cover Mapping via Weak Supervision

• URL: http://arxiv.org/abs/2406.00891v1
• Date: Sun, 2 Jun 2024 23:18:12 GMT
• Title: Global High Categorical Resolution Land Cover Mapping via Weak Supervision
• Authors: Xin-Yi Tong, Runmin Dong, Xiao Xiang Zhu,
• Abstract summary: We propose to combine fully labeled source domain and weakly labeled target domain for weakly supervised domain adaptation (WSDA) This is beneficial as the utilization of sparse and coarse weak labels can considerably alleviate the labor required for precise and detailed land cover annotation. We carry out high categorical resolution land cover mapping for 10 cities in different regions around the world, severally using PlanetScope, Gaofen-1, and Sentinel-2 satellite images.
• Score: 19.52604717907002
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Land cover information is indispensable for advancing the United Nations' sustainable development goals, and land cover mapping under a more detailed category system would significantly contribute to economic livelihood tracking and environmental degradation measurement. However, the substantial difficulty in acquiring fine-grained training data makes the implementation of this task particularly challenging. Here, we propose to combine fully labeled source domain and weakly labeled target domain for weakly supervised domain adaptation (WSDA). This is beneficial as the utilization of sparse and coarse weak labels can considerably alleviate the labor required for precise and detailed land cover annotation. Specifically, we introduce the Prototype-based pseudo-label Rectification and Expansion (PRE) approach, which leverages the prototypes (i.e., the class-wise feature centroids) as the bridge to connect sparse labels and global feature distributions. According to the feature distances to the prototypes, the confidence of pseudo-labels predicted in the unlabeled regions of the target domain is assessed. This confidence is then utilized to guide the dynamic expansion and rectification of pseudo-labels. Based on PRE, we carry out high categorical resolution land cover mapping for 10 cities in different regions around the world, severally using PlanetScope, Gaofen-1, and Sentinel-2 satellite images. In the study areas, we achieve cross-sensor, cross-category, and cross-continent WSDA, with the overall accuracy exceeding 80%. The promising results indicate that PRE is capable of reducing the dependency of land cover classification on high-quality annotations, thereby improving label efficiency. We expect our work to enable global fine-grained land cover mapping, which in turn promote Earth observation to provide more precise and thorough information for environmental monitoring.

Related papers

• AGBD: A Global-scale Biomass Dataset [18.976975819550173]
  Existing datasets for Above Ground Biomass estimation from satellite imagery are limited. This dataset combines AGB reference data from the GEDI mission with data from Sentinel-2 and PALSAR-2 imagery. It includes pre-processed high-level features such as a dense canopy height map, an elevation map, and a land-cover classification map. It can be easily accessed using a single line of code, offering a solid basis for efforts towards global AGB estimation.
  arXiv  Detail & Related papers  (2024-06-07T13:34:17Z)
• Estimating Canopy Height at Scale [15.744009072839425]
  We propose a framework for global-scale canopy height estimation based on satellite data. A comparison between predictions and ground-truth labels yields an MAE / RMSE of 2.43 / 4.73 (meters) overall and 4.45 / 6.72 (meters) for trees taller than five meters.
  arXiv  Detail & Related papers  (2024-06-03T07:53:38Z)
• Learning without Exact Guidance: Updating Large-scale High-resolution Land Cover Maps from Low-resolution Historical Labels [4.833320222969612]
  Large-scale high-resolution (HR) land-cover mapping is a vital task to survey the Earth's surface and resolve many challenges facing humanity. We propose an efficient, weakly supervised framework (Paraformer) to guide large-scale HR land-cover mapping.
  arXiv  Detail & Related papers  (2024-03-05T08:02:00Z)
• Inter-Domain Mixup for Semi-Supervised Domain Adaptation [108.40945109477886]
  Semi-supervised domain adaptation (SSDA) aims to bridge source and target domain distributions, with a small number of target labels available. Existing SSDA work fails to make full use of label information from both source and target domains for feature alignment across domains. This paper presents a novel SSDA approach, Inter-domain Mixup with Neighborhood Expansion (IDMNE), to tackle this issue.
  arXiv  Detail & Related papers  (2024-01-21T10:20:46Z)
• Progressive Feature Self-reinforcement for Weakly Supervised Semantic Segmentation [55.69128107473125]
  We propose a single-stage approach for Weakly Supervised Semantic (WSSS) with image-level labels. We adaptively partition the image content into deterministic regions (e.g., confident foreground and background) and uncertain regions (e.g., object boundaries and misclassified categories) for separate processing. Building upon this, we introduce a complementary self-enhancement method that constrains the semantic consistency between these confident regions and an augmented image with the same class labels.
  arXiv  Detail & Related papers  (2023-12-14T13:21:52Z)
• Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering [50.46892302138662]
  We address the source-free domain adaptation problem, where the source pretrained model is adapted to the target domain in the absence of source data. Our method is based on the observation that target data, which might not align with the source domain classifier, still forms clear clusters. We demonstrate that this local structure can be efficiently captured by considering the local neighbors, the reciprocal neighbors, and the expanded neighborhood.
  arXiv  Detail & Related papers  (2023-09-01T15:31:18Z)
• Generalized Universal Domain Adaptation with Generative Flow Networks [76.1350941965148]
  Generalized Universal Domain Adaptation aims to achieve precise prediction of all target labels including unknown categories. GUDA bridges the gap between label distribution shift-based and label space mismatch-based variants. We propose an active domain adaptation algorithm named GFlowDA, which selects diverse samples with probabilities proportional to a reward function.
  arXiv  Detail & Related papers  (2023-05-08T05:34:15Z)
• A Coarse-to-Fine Approach for Urban Land Use Mapping Based on Multisource Geospatial Data [4.2968261363970095]
  We propose a machine learning-based approach for parcel-level urban land use mapping. We first divide the city into built-up and non-built-up regions based on parcels generated from road networks. We then adopt different classification strategies for parcels in different regions, and finally combine the classified results into an integrated land use map.
  arXiv  Detail & Related papers  (2022-08-18T13:30:56Z)
• 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)
• Deep residential representations: Using unsupervised learning to unlock elevation data for geo-demographic prediction [0.0]
  LiDAR technology can be used to provide detailed three-dimensional elevation maps of urban and rural landscapes. To date, airborne LiDAR imaging has been predominantly confined to the environmental and archaeological domains. We consider the suitability of this data not just on its own but also as a source of data in combination with demographic features, thus providing a realistic use case for the embeddings.
  arXiv  Detail & Related papers  (2021-12-02T17:10:52Z)
• Alleviating Semantic-level Shift: A Semi-supervised Domain Adaptation Method for Semantic Segmentation [97.8552697905657]
  A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains. We propose Alleviating Semantic-level Shift (ASS), which can successfully promote the distribution consistency from both global and local views. We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes.
  arXiv  Detail & Related papers  (2020-04-02T03:25:05Z)

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.