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Found 33 papers, 17 papers with code
ExEBench: Benchmarking Foundation Models on Extreme Earth Events
ExEBench aims to (1) assess FM generalizability across diverse, high-impact tasks and domains, (2) promote the development of novel ML methods that benefit disaster management, and (3) offer a platform for analyzing the interactions and cascading effects of extreme events to advance our understanding of Earth system, especially under the climate change expected in the decades to come.
Towards a Unified Copernicus Foundation Model for Earth Vision
Advances in Earth observation (EO) foundation models have unlocked the potential of big satellite data to learn generic representations from space, benefiting a wide range of downstream applications crucial to our planet.
Panopticon: Advancing Any-Sensor Foundation Models for Earth Observation
Earth observation (EO) data features diverse sensing platforms with varying spectral bands, spatial resolutions, and sensing modalities.
GeoLangBind: Unifying Earth Observation with Agglomerative Vision-Language Foundation Models
Earth observation (EO) data, collected from diverse sensors with varying imaging principles, present significant challenges in creating unified analytical frameworks.
Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation
Earth Observation (EO) data analysis has been significantly revolutionized by deep learning (DL), with applications typically limited to grid-like data structures.
UrbanSARFloods: Sentinel-1 SLC-Based Benchmark Dataset for Urban and Open-Area Flood Mapping
Due to its cloud-penetrating capability and independence from solar illumination, satellite Synthetic Aperture Radar (SAR) is the preferred data source for large-scale flood mapping, providing global coverage and including various land cover classes.
On the Foundations of Earth and Climate Foundation Models
Foundation models have enormous potential in advancing Earth and climate sciences, however, current approaches may not be optimal as they focus on a few basic features of a desirable Earth and climate foundation model.
Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation
The development of foundation models has revolutionized our ability to interpret the Earth's surface using satellite observational data.
Causal Graph Neural Networks for Wildfire Danger Prediction
In that direction, we propose integrating causality with Graph Neural Networks (GNNs) that explicitly model the causal mechanism among complex variables via graph learning.
ChatEarthNet: A Global-Scale Image-Text Dataset Empowering Vision-Language Geo-Foundation Models
In this context, we introduce a global-scale, high-quality image-text dataset for remote sensing, providing natural language descriptions for Sentinel-2 data to facilitate the understanding of satellite imagery for common users.
Efficient Subseasonal Weather Forecast using Teleconnection-informed Transformers
Subseasonal forecasting, which is pivotal for agriculture, water resource management, and early warning of disasters, faces challenges due to the chaotic nature of the atmosphere.
SkyEyeGPT: Unifying Remote Sensing Vision-Language Tasks via Instruction Tuning with Large Language Model
Specifically, after projecting RS visual features to the language domain via an alignment layer, they are fed jointly with task-specific instructions into an LLM-based RS decoder to predict answers for RS open-ended tasks.
One for All: Toward Unified Foundation Models for Earth Vision
Current remote sensing foundation models typically specialize in a single modality or a specific spatial resolution range, limiting their versatility for downstream datasets.
Mono3DVG: 3D Visual Grounding in Monocular Images
To foster this task, we propose Mono3DVG-TR, an end-to-end transformer-based network, which takes advantage of both the appearance and geometry information in text embeddings for multi-modal learning and 3D object localization.
Ranked #1 on
Mono3DVG
on Mono3DRefer
HTC-DC Net: Monocular Height Estimation from Single Remote Sensing Images
To tackle this problem, we propose a method for monocular height estimation from optical imagery, which is currently one of the richest sources of remote sensing data.
Few-shot Object Detection in Remote Sensing: Lifting the Curse of Incompletely Annotated Novel Objects
In this context, few-shot object detection (FSOD) has emerged as a promising direction, which aims at enabling the model to detect novel objects with only few of them annotated.
Decoupling Common and Unique Representations for Multimodal Self-supervised Learning
The increasing availability of multi-sensor data sparks wide interest in multimodal self-supervised learning.
Exploring Geometric Deep Learning For Precipitation Nowcasting
Motivated by this, we explore a geometric deep learning-based temporal Graph Convolutional Network (GCN) for precipitation nowcasting.
Parameter-Efficient Transfer Learning for Remote Sensing Image-Text Retrieval
To address this issue, in this work, we investigate the parameter-efficient transfer learning (PETL) method to effectively and efficiently transfer visual-language knowledge from the natural domain to the RS domain on the image-text retrieval task.
Ranked #5 on
Cross-Modal Retrieval
on RSICD
PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Clouds
We present PolyGNN, a polyhedron-based graph neural network for 3D building reconstruction from point clouds.
RSSOD-Bench: A large-scale benchmark dataset for Salient Object Detection in Optical Remote Sensing Imagery
We present the RSSOD-Bench dataset for salient object detection (SOD) in optical remote sensing imagery.
GAMUS: A Geometry-aware Multi-modal Semantic Segmentation Benchmark for Remote Sensing Data
Towards a fair and comprehensive analysis of existing methods, the proposed benchmark consists of 1) a large-scale dataset including co-registered RGB and nDSM pairs and pixel-wise semantic labels; 2) a comprehensive evaluation and analysis of existing multi-modal fusion strategies for both convolutional and Transformer-based networks on remote sensing data.
Ranked #1 on
Semantic Segmentation
on GAMUS
SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation
Self-supervised pre-training bears potential to generate expressive representations without human annotation.
Ranked #1 on
Multi-Label Image Classification
on BigEarthNet (official test set)
(using extra training data)
RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data
However, the object-level visual grounding on RS images is still under-explored.
EarthNets: Empowering AI in Earth Observation
Furthermore, a new platform for EO, termed EarthNets, is released to achieve a fair and consistent evaluation of deep learning methods on remote sensing data.
Doubly Deformable Aggregation of Covariance Matrices for Few-shot Segmentation
To address this problem, we propose to aggregate the learnable covariance matrices with a deformable 4D Transformer to effectively predict the segmentation map.
Ranked #1 on
Few-Shot Semantic Segmentation
on FSS-1000 (5-shot)
Disentangled Latent Transformer for Interpretable Monocular Height Estimation
Furthermore, a novel unsupervised semantic segmentation task based on height estimation is first introduced in this work.
THE Benchmark: Transferable Representation Learning for Monocular Height Estimation
Therefore, we propose a new benchmark dataset to study the transferability of height estimation models in a cross-dataset setting.
Change Detection Meets Visual Question Answering
In order to provide every user with flexible access to change information and help them better understand land-cover changes, we introduce a novel task: change detection-based visual question answering (CDVQA) on multi-temporal aerial images.
ASK: Adaptively Selecting Key Local Features for RGB-D Scene Recognition
Discriminative local theme-level and object-level representations can be selected with the DLFS module from the spatially-correlated multi-modal RGB-D features.
CM-Net: Concentric Mask based Arbitrary-Shaped Text Detection
Extensive experiments demonstrate the proposed CM is efficient and robust to fit arbitrary-shaped text instances, and also validate the effectiveness of MPF and constraints loss for discriminative text features recognition.
Variational Context-Deformable ConvNets for Indoor Scene Parsing
The main contributions of this work are as follows: 1) a novel VCD module is proposed, which exploits learnable Gaussian kernels to enable feature learning with structured adaptive-context; 2) variational Bayesian probabilistic modeling is introduced for the training of VCD module, which can make it continuous and more stable; 3) a perspective-aware guidance module is designed to take advantage of multi-modal information for RGB-D segmentation.
Ranked #1 on
Scene Parsing
on Cityscapes test
VSSA-NET: Vertical Spatial Sequence Attention Network for Traffic Sign Detection
Our contributions are as follows: 1) We propose a multi-resolution feature fusion network architecture which exploits densely connected deconvolution layers with skip connections, and can learn more effective features for the small size object; 2) We frame the traffic sign detection as a spatial sequence classification and regression task, and propose a vertical spatial sequence attention (VSSA) module to gain more context information for better detection performance.
