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Found 16 papers, 5 papers with code
Bridging Data Islands: Geographic Heterogeneity-Aware Federated Learning for Collaborative Remote Sensing Semantic Segmentation
Remote sensing semantic segmentation (RSS) is an essential task in Earth Observation missions.
AUG: A New Dataset and An Efficient Model for Aerial Image Urban Scene Graph Generation
To fill in the gap of the overhead view dataset, this paper constructs and releases an aerial image urban scene graph generation (AUG) dataset.
AllSpark: A Multimodal Spatio-Temporal General Intelligence Model with Thirteen Modalities
We introduce the Language as Reference Framework (LaRF), a fundamental principle for constructing a multimodal unified model, aiming to strike a trade-off between the cohesion and autonomy among different modalities.
SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery
Prior studies on Remote Sensing Foundation Model (RSFM) reveal immense potential towards a generic model for Earth Observation.
Learning to Holistically Detect Bridges from Large-Size VHR Remote Sensing Imagery
Therefore, to ensure the visibility and integrity of bridges, it is essential to perform holistic bridge detection in large-size very-high-resolution (VHR) RSIs.
PointOBB: Learning Oriented Object Detection via Single Point Supervision
Single point-supervised object detection is gaining attention due to its cost-effectiveness.
GLH-Water: A Large-Scale Dataset for Global Surface Water Detection in Large-Size Very-High-Resolution Satellite Imagery
Global surface water detection in very-high-resolution (VHR) satellite imagery can directly serve major applications such as refined flood mapping and water resource assessment.
Progressive Learning with Cross-Window Consistency for Semi-Supervised Semantic Segmentation
Semi-supervised semantic segmentation focuses on the exploration of a small amount of labeled data and a large amount of unlabeled data, which is more in line with the demands of real-world image understanding applications.
EHSNet: End-to-End Holistic Learning Network for Large-Size Remote Sensing Image Semantic Segmentation
This paper presents EHSNet, a new end-to-end segmentation network designed for the holistic learning of large-size remote sensing image semantic segmentation (LRISS).
Dark Spot Detection from SAR Images Based on Superpixel Deeper Graph Convolutional Network
Dark spot detection is the first step in the detection of oil spills, which then become oil slick candidates.
Hierarchical Memory Learning for Fine-Grained Scene Graph Generation
After the autonomous partition of coarse and fine predicates, the model is first trained on the coarse predicates and then learns the fine predicates.
Learning Soft Estimator of Keypoint Scale and Orientation With Probabilistic Covariant Loss
First, the estimator is constructed to predict the discrete distributions of scales and orientations.
RMNA: A Neighbor Aggregation-Based Knowledge Graph Representation Learning Model Using Rule Mining
Therefore, neighbor aggregation-based representation learning (NARL) models are proposed, which encode the information in the neighbors of an entity into its embeddings.
Group-Aware Graph Neural Network for Nationwide City Air Quality Forecasting
The model constructs a city graph and a city group graph to model the spatial and latent dependencies between cities, respectively.
Collaboratively boosting data-driven deep learning and knowledge-guided ontological reasoning for semantic segmentation of remote sensing imagery
As one kind of architecture from the deep learning family, deep semantic segmentation network (DSSN) achieves a certain degree of success on the semantic segmentation task and obviously outperforms the traditional methods based on hand-crafted features.
Segmentation Of Remote Sensing Imagery
Semantic Segmentation
Spatial Information Inference Net: Road Extraction Using Road-Specific Contextual Information
The validation experiments using three large datasets of very high-resolution (VHR) satellite imagery show that the proposed method can improve road extraction accuracy and provide an output that is more in line with human expectations.
