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Found 18 papers, 10 papers with code
LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote Sensing Vision-Language Interpretation
In this study, we introduce LHRS-Bot-Nova, an MLLM specialized in understanding remote sensing (RS) images, designed to expertly perform a wide range of RS understanding tasks aligned with human instructions.
Contrastive Pretraining for Visual Concept Explanations of Socioeconomic Outcomes
Further, we illustrate how analyzing the model's conceptual sensitivity for the intervals of socioeconomic outcomes can shed light on new insights for urban studies.
Building Footprint Extraction with Graph Convolutional Network
In this work, we have proposed a end-to-end framework to overcome this issue, which uses the graph convolutional network (GCN) for building footprint extraction task.
MH-DETR: Video Moment and Highlight Detection with Cross-modal Transformer
With the increasing demand for video understanding, video moment and highlight detection (MHD) has emerged as a critical research topic.
Large-Scale Land Cover Mapping with Fine-Grained Classes via Class-Aware Semi-Supervised Semantic Segmentation
Moreover, we propose the Class Center Contrast method to jointly utilize the labeled and unlabeled data.
ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery
The potential for impact and scale of leveraging advancements in machine learning and remote sensing technologies is promising but needs to be of high quality in order to replace the current forest stock protocols for certifications.
SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal
About half of all optical observations collected via spaceborne satellites are affected by haze or clouds.
Ranked #3 on
Cloud Removal
on SEN12MS-CR-TS
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.
Lebanon Solar Rooftop Potential Assessment using Buildings Segmentation from Aerial Images
Factors such as size, ground coverage ratio and PV_out are carefully investigated for each district.
SCIDA: Self-Correction Integrated Domain Adaptation from Single- to Multi-label Aerial Images
In our experiments, trained with single-labeled MAI-AID-s and MAI-UCM-s datasets, the proposed model is tested directly on our collected Multi-scene Aerial Image (MAI) dataset.
Tackling the Overestimation of Forest Carbon with Deep Learning and Aerial Imagery
This proposal paper describes the first systematic comparison of forest carbon estimation from aerial imagery, satellite imagery, and ground-truth field measurements via deep learning-based algorithms for a tropical reforestation project.
Towards SAR Tomographic Inversion via Sparse Bayesian Learning
Existing SAR tomography (TomoSAR) algorithms are mostly based on an inversion of the SAR imaging model, which are often computationally expensive.
Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery
This work has been accepted by IEEE TGRS for publication.
Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution
The recent advancement of deep learning techniques has made great progress on hyperspectral image super-resolution (HSI-SR).
Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition
With the help of this dataset, we evaluate three proposed approaches for transferring the sound event knowledge to the aerial scene recognition task in a multimodal learning framework, and show the benefit of exploiting the audio information for the aerial scene recognition.
GeoSay: A Geometric Saliency for Extracting Buildings in Remote Sensing Images
More precisely, given an image, the geometric saliency is derived from a mid-level geometric representations based on meaningful junctions that can locally describe geometrical structures of images.
A Conditional Generative Adversarial Network to Fuse Sar And Multispectral Optical Data For Cloud Removal From Sentinel-2 Images
In this paper, we present the first conditional generative adversarial network (cGAN) architecture that is specifically designed to fuse synthetic aperture radar (SAR) and optical multi-spectral (MS) image data to generate cloud- and haze-free MS optical data from a cloud-corrupted MS input and an auxiliary SAR image.
Ranked #6 on
Cloud Removal
on SEN12MS-CR
Joint & Progressive Learning from High-Dimensional Data for Multi-Label Classification
Despite the fact that nonlinear subspace learning techniques (e. g. manifold learning) have successfully applied to data representation, there is still room for improvement in explainability (explicit mapping), generalization (out-of-samples), and cost-effectiveness (linearization).
