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Found 145 papers, 56 papers with code
True or False? Detecting False Information on Social Media Using Graph Neural Networks
In recent years, false information such as fake news, rumors and conspiracy theories on many relevant issues in society have proliferated.
MaskCD: A Remote Sensing Change Detection Network Based on Mask Classification
Change detection (CD) from remote sensing (RS) images using deep learning has been widely investigated in the literature.
Neural Plasticity-Inspired Foundation Model for Observing the Earth Crossing Modalities
The development of foundation models has revolutionized our ability to interpret the Earth's surface using satellite observational data.
Large-scale flood modeling and forecasting with FloodCast
Large-scale hydrodynamic models generally rely on fixed-resolution spatial grids and model parameters as well as incurring a high computational cost.
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.
AIO2: Online Correction of Object Labels for Deep Learning with Incomplete Annotation in Remote Sensing Image Segmentation
AIO2 utilizes a mean teacher model to enhance training robustness with noisy labels to both stabilize the training accuracy curve for fitting in ACT and provide pseudo labels for correction in O2C.
Task Specific Pretraining with Noisy Labels for Remote sensing Image Segmentation
In this work, we propose to explore the under-exploited potential of noisy labels for segmentation task specific pretraining, and exam its robustness when confronted with mismatched categories and different decoders during fine-tuning.
Opening the Black-Box: A Systematic Review on Explainable AI in Remote Sensing
In recent years, black-box machine learning approaches have become a dominant modeling paradigm for knowledge extraction in Remote Sensing.
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.
PixelDINO: Semi-Supervised Semantic Segmentation for Detecting Permafrost Disturbances
To improve model generalization across the Arctic without the need for additional labelled data, we present a semi-supervised learning approach to train semantic segmentation models to detect RTS.
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.
Towards Label Embedding -- Measuring classification difficulty
In supervised learning, uncertainty can already occur in the very first stage of the training process, the labelling step.
Feature Guided Masked Autoencoder for Self-supervised Learning in Remote Sensing
Self-supervised learning guided by masked image modelling, such as Masked AutoEncoder (MAE), has attracted wide attention for pretraining vision transformers in remote sensing.
Ranked #1 on
Multi-Label Image Classification
on BigEarthNet-S1 (official test set)
(using extra training data)
Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology
Specifically, we present a comprehensive review of the physics-aware ML methods, building a structured community (PaML) of existing methodologies that integrate prior physical knowledge or physics-based modeling into ML.
HyperLISTA-ABT: An Ultra-light Unfolded Network for Accurate Multi-component Differential Tomographic SAR Inversion
Deep neural networks based on unrolled iterative algorithms have achieved remarkable success in sparse reconstruction applications, such as synthetic aperture radar (SAR) tomographic inversion (TomoSAR).
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.
Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks
Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-modality-dominated remote sensing (RS) applications, especially with an emphasis on individual urban environments (e. g., single cities or regions).
Self-supervised Domain-agnostic Domain Adaptation for Satellite Images
A promising method to address this problem is domain adaptation, where the training and the testing datasets are split into two or multiple domains according to their distributions, and an adaptation method is applied to improve the generalizability of the model on the testing dataset.
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.
Towards Large-scale Building Attribute Mapping using Crowdsourced Images: Scene Text Recognition on Flickr and Problems to be Solved
This work addresses the challenges in applying Scene Text Recognition (STR) in crowdsourced street-view images for building attribute mapping.
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.
DeCUR: decoupling common & unique representations for multimodal self-supervision
We propose Decoupling Common and Unique Representations (DeCUR), a simple yet effective method for multimodal self-supervised learning.
Deep Semantic Model Fusion for Ancient Agricultural Terrace Detection
Discovering ancient agricultural terraces in desert regions is important for the monitoring of long-term climate changes on the Earth's surface.
UCDFormer: Unsupervised Change Detection Using a Transformer-driven Image Translation
Specifically, a transformer-driven image translation composed of a light-weight transformer and a domain-specific affinity weight is first proposed to mitigate domain shift between two images with real-time efficiency.
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.
A Deep Active Contour Model for Delineating Glacier Calving Fronts
Building on this observation, we completely rephrase the task as a contour tracing problem and propose a model for explicit contour detection that does not incorporate any dense predictions as intermediate steps.
Semi-Supervised Learning for hyperspectral images by non parametrically predicting view assignment
Recently, to effectively train the deep learning models with minimal labelled samples, the unlabeled samples are also being leveraged in self-supervised and semi-supervised setting.
DisasterNets: Embedding Machine Learning in Disaster Mapping
It consists of two stages, space granulation and attribute granulation.
RRSIS: Referring Remote Sensing Image Segmentation
Localizing desired objects from remote sensing images is of great use in practical applications.
GEO-Bench: Toward Foundation Models for Earth Monitoring
Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to downstream tasks.
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.
Overcoming Language Bias in Remote Sensing Visual Question Answering via Adversarial Training
Based on the adversarial branch, we introduce two regularizers to constrain the training process against language bias.
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
Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography
An emerging technique known as deep unrolling provided a good combination of the descriptive ability of neural networks, explainable, and computational efficiency for BPDN.
Artificial intelligence to advance Earth observation: a perspective
Earth observation (EO) is a prime instrument for monitoring land and ocean processes, studying the dynamics at work, and taking the pulse of our planet.
Vision-Language Models in Remote Sensing: Current Progress and Future Trends
Existing AI-related research in remote sensing primarily focuses on visual understanding tasks while neglecting the semantic understanding of the objects and their relationships.
High Quality Large-Scale 3-D Urban Mapping with Multi-Master TomoSAR
Multi-baseline interferometric synthetic aperture radar (InSAR) techniques are effective approaches for retrieving the 3-D information of urban areas.
Deep Unsupervised Learning for 3D ALS Point Cloud Change Detection
To circumnavigate this dependence, we propose an unsupervised 3D point cloud change detection method mainly based on self-supervised learning using deep clustering and contrastive learning.
The Politics of Language Choice: How the Russian-Ukrainian War Influences Ukrainians' Language Use on Twitter
Notably, we find that more than half of the Russian-tweeting users shift towards Ukrainian as a result of the war.
UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series
Clouds and haze often occlude optical satellite images, hindering continuous, dense monitoring of the Earth's surface.
Ranked #1 on
Cloud Removal
on SEN12MS-CR
Multilingual Augmentation for Robust Visual Question Answering in Remote Sensing Images
With the proposed augmented dataset, we are able to obtain more questions in addition to the original ones with the same meaning.
Universal Domain Adaptation for Remote Sensing Image Scene Classification
Empirical results show that the proposed model is effective and practical for remote sensing image scene classification, regardless of whether the source data is available or not.
High-Resolution Cloud Removal with Multi-Modal and Multi-Resolution Data Fusion: A New Baseline and Benchmark
In this paper, we introduce Planet-CR, a benchmark dataset for high-resolution cloud removal with multi-modal and multi-resolution data fusion.
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)
Geo-Information Harvesting from Social Media Data
In this article, we address key aspects in the field, including data availability, analysis-ready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data.
Exploring Self-Attention for Crop-type Classification Explainability
Using explainable machine learning to reveal the inner workings of these models is an important step towards improving stakeholders' trust and efficient agriculture monitoring.
Automatic separation of laminar-turbulent flows on aircraft wings and stabilisers via adaptive attention butterfly network
Many of the laminar-turbulent flow localisation techniques are strongly dependent upon expert control even-though determining the flow distribution is the prerequisite for analysing the efficiency of wing & stabiliser design in aeronautics.
Laminar-Turbulent Flow Localisation
Self-Supervised Learning
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.
Anomaly Detection in Aerial Videos with Transformers
In this paper, we create a new dataset, named DroneAnomaly, for anomaly detection in aerial videos.
FuTH-Net: Fusing Temporal Relations and Holistic Features for Aerial Video Classification
Furthermore, the holistic features are refined by the multi-scale temporal relations in a novel fusion module for yielding more discriminative video representations.
The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery
The objective of the competition is to automatically detect landslides based on large-scale multiple sources of satellite imagery collected globally.
Enabling Country-Scale Land Cover Mapping with Meter-Resolution Satellite Imagery
To validate the generalizability of our dataset and the proposed approach across different sensors and different geographical regions, we carry out land cover mapping on five megacities in China and six cities in other five Asian countries severally using: PlanetScope (3 m), Gaofen-1 (8 m), and Sentinel-2 (10 m) satellite images.
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)
Self-supervised Learning in Remote Sensing: A Review
In deep learning research, self-supervised learning (SSL) has received great attention triggering interest within both the computer vision and remote sensing communities.
Ranked #3 on
Multi-Label Image Classification
on BigEarthNet
Self Supervised Learning for Few Shot Hyperspectral Image Classification
Deep learning has proven to be a very effective approach for Hyperspectral Image (HSI) classification.
Understanding the Role of Weather Data for Earth Surface Forecasting using a ConvLSTM-based Model
Climate change is perhaps the biggest single threat to humankind and the environment, as it severely impacts our terrestrial surface, home to most of the living species.
Ranked #2 on
Earth Surface Forecasting
on EarthNet2021 OOD Track
Monitoring Urban Forests from Auto-Generated Segmentation Maps
We present and evaluate a weakly-supervised methodology to quantify the spatio-temporal distribution of urban forests based on remotely sensed data with close-to-zero human interaction.
GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion
The challenge of the cloud removal task can be alleviated with the aid of Synthetic Aperture Radar (SAR) images that can penetrate cloud cover.
Ranked #2 on
Cloud Removal
on SEN12MS-CR
Going Beyond One-Hot Encoding in Classification: Can Human Uncertainty Improve Model Performance?
Technological and computational advances continuously drive forward the broad field of deep learning.
Semi-Supervised Building Footprint Generation with Feature and Output Consistency Training
Considering that rich information is also encoded in feature maps, we propose to integrate the consistency of both features and outputs in the end-to-end network training of unlabeled samples, enabling to impose additional constraints.
From Easy to Hard: Learning Language-guided Curriculum for Visual Question Answering on Remote Sensing Data
To be more specific, a language-guided SPCL method with a soft weighting strategy is explored in this work.
Self-supervised Vision Transformers for Joint SAR-optical Representation Learning
Experimental results employing the BigEarthNet-MM dataset demonstrate the benefits of both, the ViT backbones and the proposed multimodal SSL algorithm DINO-MM.
So2Sat POP -- A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale
Obtaining a dynamic population distribution is key to many decision-making processes such as urban planning, disaster management and most importantly helping the government to better allocate socio-technical supply.
DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation
These observations are paired with pixel-wise monthly semantic segmentation labels of 7 land use and land cover (LULC) classes.
Using Social Media Images for Building Function Classification
To cope with this issue this study proposes a filtering pipeline to yield high quality, ground level imagery from large social media image datasets.
Landscape of Neural Architecture Search across sensors: how much do they differ ?
With the rapid rise of neural architecture search, the ability to understand its complexity from the perspective of a search algorithm is desirable.
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.
$\boldsymbolγ$-Net: Superresolving SAR Tomographic Inversion via Deep Learning
This superior performance comes at the cost of extra computational burdens, because of the sparse reconstruction, which cannot be solved analytically and we need to employ computationally expensive iterative solvers.
Large-scale Building Height Retrieval from Single SAR Imagery based on Bounding Box Regression Networks
Building height retrieval from synthetic aperture radar (SAR) imagery is of great importance for urban applications, yet highly challenging owing to the complexity of SAR data.
A Data-driven Approach to Neural Architecture Search Initialization
First, we perform a calibrated clustering analysis of the search space, and second, we extract the centroids and use them to initialize a NAS algorithm.
Fitness Landscape Footprint: A Framework to Compare Neural Architecture Search Problems
In this paper, we propose to use fitness landscape analysis to study a neural architecture search problem.
SurroundNet: Towards Effective Low-Light Image Enhancement
Although Convolution Neural Networks (CNNs) has made substantial progress in the low-light image enhancement task, one critical problem of CNNs is the paradox of model complexity and performance.
Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images
It uses this model to analyze differences in the pixel and its spatial context-based predictions in subsequent time periods for change detection.
Reiterative Domain Aware Multi-Target Adaptation
Multi-target domain adaptation is a powerful extension in which a single classifier is learned for multiple unlabeled target domains.
Lessons from the Clustering Analysis of a Search Space: A Centroid-based Approach to Initializing NAS
In this study, we propose to accelerate a NAS algorithm using a data-driven initialization technique, leveraging the availability of NAS benchmarks.
Under the Radar -- Auditing Fairness in ML for Humanitarian Mapping
Our findings indicate that poverty is systematically overestimated and electricity systematically underestimated for scheduled tribes in comparison to a synthetic counterfactual group of villages.
Self-supervised Audiovisual Representation Learning for Remote Sensing Data
By fine-tuning the models on a number of commonly used remote sensing datasets, we show that our approach outperforms existing pre-training strategies for remote sensing imagery.
Ranked #2 on
Cross-Modal Retrieval
on SoundingEarth
Segmentation of VHR EO Images using Unsupervised Learning
The proposed method exploits this property to sample smaller patches from the larger scene and uses deep clustering and contrastive learning to refine the weights of a lightweight deep model composed of a series of the convolution layers along with an embedded channel attention.
A Survey of Uncertainty in Deep Neural Networks
Different examples from the wide spectrum of challenges in different fields give an idea of the needs and challenges regarding uncertainties in practical applications.
Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model
Moreover, to better assess multimodal baselines and the newly-proposed S2FL model, three multimodal RS benchmark datasets, i. e., Houston2013 -- hyperspectral and multispectral data, Berlin -- hyperspectral and synthetic aperture radar (SAR) data, Augsburg -- hyperspectral, SAR, and digital surface model (DSM) data, are released and used for land cover classification.
A lightweight deep learning based cloud detection method for Sentinel-2A imagery fusing multi-scale spectral and spatial features
In the encoder, three input branches are designed to handle spectral bands at their native resolution and extract multiscale spectral features.
Generative modeling of spatio-temporal weather patterns with extreme event conditioning
Deep generative models are increasingly used to gain insights in the geospatial data domain, e. g., for climate data.
Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networks
To be more specific, we first learn the prototype representation of each aerial scene from single-scene aerial image datasets and store it in an external memory.
Towards a Collective Agenda on AI for Earth Science Data Analysis
In the last years we have witnessed the fields of geosciences and remote sensing and artificial intelligence to become closer.
Trusting small training dataset for supervised change detection
Deep learning (DL) based supervised change detection (CD) models require large labeled training data.
Out-of-distribution detection in satellite image classification
In satellite image analysis, distributional mismatch between the training and test data may arise due to several reasons, including unseen classes in the test data and differences in the geographic area.
MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images
We conduct experiments with extensive baseline models on both MultiScene-Clean and MultiScene to offer benchmarks for multi-scene recognition in single images and learning from noisy labels for this task, respectively.
Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification
To this end, we frame the problem of unsupervised band selection as a Markov decision process, propose an effective method to parameterize it, and finally solve the problem by deep reinforcement learning.
HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline
Deep learning-based coastline detection algorithms have begun to outshine traditional statistical methods in recent years.
Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing
Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS).
Adversarial Shape Learning for Building Extraction in VHR Remote Sensing Images
To address this issue, we propose an adversarial shape learning network (ASLNet) to model the building shape patterns that improve the accuracy of building segmentation.
Self-Supervised Multisensor Change Detection
In particular, we are interested in the combination of the images acquired by optical and Synthetic Aperture Radar (SAR) sensors.
Towards Bridging the gap between Empirical and Certified Robustness against Adversarial Examples
In this paper, we propose a novel method, called \emph{Certification through Adaptation}, that transforms an AT model into a randomized smoothing classifier during inference to provide certified robustness for $\ell_2$ norm without affecting their empirical robustness against adversarial attacks.
Semantic Segmentation of Remote Sensing Images with Sparse Annotations
Training Convolutional Neural Networks (CNNs) for very high resolution images requires a large quantity of high-quality pixel-level annotations, which is extremely labor- and time-consuming to produce.
Exploring Deep 3D Spatial Encodings for Large-Scale 3D Scene Understanding
In this letter, we have proposed an alternative approach to overcome the limitations of CNN based approaches by encoding the spatial features of raw 3D point clouds into undirected symmetrical graph models.
Multi-task Learning for Human Settlement Extent Regression and Local Climate Zone Classification
Human Settlement Extent (HSE) and Local Climate Zone (LCZ) maps are both essential sources, e. g., for sustainable urban development and Urban Heat Island (UHI) studies.
CG-Net: Conditional GIS-aware Network for Individual Building Segmentation in VHR SAR Images
Object retrieval and reconstruction from very high resolution (VHR) synthetic aperture radar (SAR) images are of great importance for urban SAR applications, yet highly challenging owing to the complexity of SAR data.
A Novel Actor Dual-Critic Model for Remote Sensing Image Captioning
We deal with the problem of generating textual captions from optical remote sensing (RS) images using the notion of deep reinforcement learning.
Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality Reduction
Conventional nonlinear subspace learning techniques (e. g., manifold learning) usually introduce some drawbacks in explainability (explicit mapping) and cost-effectiveness (linearization), generalization capability (out-of-sample), and representability (spatial-spectral discrimination).
SAR Tomography via Nonlinear Blind Scatterer Separation
Layover separation has been fundamental to many synthetic aperture radar applications, such as building reconstruction and biomass estimation.
Cross-language sentiment analysis of European Twitter messages duringthe COVID-19 pandemic
We separate the results by country of origin, and correlate their temporal development with events in those countries.
Generative Adversarial Networks for Synthesizing InSAR Patches
Generative Adversarial Networks (GANs) have been employed with certain success for image translation tasks between optical and real-valued SAR intensity imagery.
Spatial-Spectral Manifold Embedding of Hyperspectral Data
In recent years, hyperspectral imaging, also known as imaging spectroscopy, has been paid an increasing interest in geoscience and remote sensing community.
Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion
Optical remote sensing imagery is at the core of many Earth observation activities.
Ranked #3 on
Cloud Removal
on SEN12MS-CR
Cross-language sentiment analysis of European Twitter messages during the COVID-19 pandemic
In this paper, we analyze Twitter messages (tweets) collected during the first months of the COVID-19 pandemic in Europe with regard to their sentiment.
Single-Look Multi-Master SAR Tomography: An Introduction
This paper addresses the general problem of single-look multi-master SAR tomography.
X-ModalNet: A Semi-Supervised Deep Cross-Modal Network for Classification of Remote Sensing Data
This paper addresses the problem of semi-supervised transfer learning with limited cross-modality data in remote sensing.
On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances and Million-AID
After reviewing existing benchmark datasets in the research community of RS image interpretation, this article discusses the problem of how to efficiently prepare a suitable benchmark dataset for RS image interpretation.
Deep Learning Meets SAR
Deep learning in remote sensing has become an international hype, but it is mostly limited to the evaluation of optical data.
Instance segmentation of buildings using keypoints
The detected keypoints are subsequently reformulated as a closed polygon, which is the semantic boundary of the building.
Multi-level Feature Fusion-based CNN for Local Climate Zone Classification from Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset
As a unique classification scheme for urban forms and functions, the local climate zone (LCZ) system provides essential general information for any studies related to urban environments, especially on a large scale.
Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions
Visual crowd counting has been recently studied as a way to enable people counting in crowd scenes from images.
SAR Tomography at the Limit: Building Height Reconstruction Using Only 3-5 TanDEM-X Bistatic Interferograms
Multi-baseline interferometric synthetic aperture radar (InSAR) techniques are effective approaches for retrieving the 3-D information of urban areas.
Weakly Supervised Semantic Segmentation of Satellite Images for Land Cover Mapping -- Challenges and Opportunities
Therefore, this paper seeks to make a case for the application of weakly supervised learning strategies to get the most out of available data sources and achieve progress in high-resolution large-scale land cover mapping.
Weakly-supervised Learning
Weakly supervised Semantic Segmentation
+1
Building Footprint Generation by IntegratingConvolution Neural Network with Feature PairwiseConditional Random Field (FPCRF)
Due to the complexity of buildings, the accurate and reliable generation of the building footprint from remote sensing imagery is still a challenging task.
ERA: A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos
In this paper, we introduce a novel problem of event recognition in unconstrained aerial videos in the remote sensing community and present a large-scale, human-annotated dataset, named ERA (Event Recognition in Aerial videos), consisting of 2, 864 videos each with a label from 25 different classes corresponding to an event unfolding 5 seconds.
So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification
This is especially true for an automated analysis of remote sensing images on a global scale, which enables us to address global challenges such as urbanization and climate change using state-of-the-art machine learning techniques.
BIG-bench Machine Learning
Cultural Vocal Bursts Intensity Prediction
+1
Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification
In this paper, we propose a solution to address this issue by locally extracting invariant features from hyperspectral imagery (HSI) in both spatial and frequency domains, using a method called invariant attribute profiles (IAPs).
Learning Shared Cross-modality Representation Using Multispectral-LiDAR and Hyperspectral Data
Due to the ever-growing diversity of the data source, multi-modality feature learning has attracted more and more attention.
Building Segmentation through a Gated Graph Convolutional Neural Network with Deep Structured Feature Embedding
Taking the semantic segmentation of building footprints as a practical example, we compared different feature embedding architectures and graph neural networks.
Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation
3D Point Cloud Semantic Segmentation (PCSS) is attracting increasing interest, due to its applicability in remote sensing, computer vision and robotics, and due to the new possibilities offered by deep learning techniques.
Relation Network for Multi-label Aerial Image Classification
Particularly, our network consists of three elemental modules: 1) a label-wise feature parcel learning module, 2) an attentional region extraction module, and 3) a label relational inference module.
SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion
The availability of curated large-scale training data is a crucial factor for the development of well-generalizing deep learning methods for the extraction of geoinformation from multi-sensor remote sensing imagery.
MIMA: MAPPER-Induced Manifold Alignment for Semi-Supervised Fusion of Optical Image and Polarimetric SAR Data
Multi-modal data fusion has recently been shown promise in classification tasks in remote sensing.
Fusion of Heterogeneous Earth Observation Data for the Classification of Local Climate Zones
This paper proposes a novel framework for fusing multi-temporal, multispectral satellite images and OpenStreetMap (OSM) data for the classification of local climate zones (LCZs).
A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial Scenes
Most current semantic segmentation approaches fall back on deep convolutional neural networks (CNNs).
Learnable Manifold Alignment (LeMA) : A Semi-supervised Cross-modality Learning Framework for Land Cover and Land Use Classification
In this paper, we aim at tackling a general but interesting cross-modality feature learning question in remote sensing community --- can a limited amount of highly-discrimin-ative (e. g., hyperspectral) training data improve the performance of a classification task using a large amount of poorly-discriminative (e. g., multispectral) data?
CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences
To achieve accurate land cover classification over a large coverage, we propose a cross-modality feature learning framework, called common subspace learning (CoSpace), by jointly considering subspace learning and supervised classification.
Non-Local Compressive Sensing Based SAR Tomography
We propose to increase SNR by integrating non-local estimation into the inversion and show that a reasonable reconstruction of buildings from only seven interferograms is feasible.
An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing
To this end, we propose a novel spectral mixture model, called the augmented linear mixing model (ALMM), to address spectral variability by applying a data-driven learning strategy in inverse problems of hyperspectral unmixing.
Building Footprint Generation Using Improved Generative Adversarial Networks
The automatic generation of building footprints from satellite images presents a considerable challenge due to the complexity of building shapes.
R$^3$-Net: A Deep Network for Multi-oriented Vehicle Detection in Aerial Images and Videos
In this paper, we propose a novel deep network, called rotatable region-based residual network (R$^3$-Net), to detect multi-oriented vehicles in aerial images and videos.
Buildings Detection in VHR SAR Images Using Fully Convolution Neural Networks
This paper addresses the highly challenging problem of automatically detecting man-made structures especially buildings in very high resolution (VHR) synthetic aperture radar (SAR) images.
Recurrently Exploring Class-wise Attention in A Hybrid Convolutional and Bidirectional LSTM Network for Multi-label Aerial Image Classification
The proposed network consists of three indispensable components: 1) a feature extraction module, 2) a class attention learning layer, and 3) a bidirectional LSTM-based sub-network.
The SEN1-2 Dataset for Deep Learning in SAR-Optical Data Fusion
While deep learning techniques have an increasing impact on many technical fields, gathering sufficient amounts of training data is a challenging problem in remote sensing.
Vehicle Instance Segmentation from Aerial Image and Video Using a Multi-Task Learning Residual Fully Convolutional Network
We propose to tackle this problem with a semantic boundary-aware multi-task learning network.
RiFCN: Recurrent Network in Fully Convolutional Network for Semantic Segmentation of High Resolution Remote Sensing Images
The former is a classification CNN architecture for feature extraction, which takes an input image and produces multi-level convolutional feature maps from shallow to deep; while in the later, to achieve accurate boundary inference and semantic segmentation, boundary-aware high resolution feature maps in shallower layers and high-level but low-resolution features are recursively embedded into the learning framework (from deep to shallow) to generate a fused feature representation that draws a holistic picture of not only high-level semantic information but also low-level fine-grained details.
Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery
As far as we know, this is the first time that a recurrent convolutional network architecture has been proposed for multitemporal remote sensing image analysis.
IM2HEIGHT: Height Estimation from Single Monocular Imagery via Fully Residual Convolutional-Deconvolutional Network
In this paper we tackle a very novel problem, namely height estimation from a single monocular remote sensing image, which is inherently ambiguous, and a technically ill-posed problem, with a large source of uncertainty coming from the overall scale.
Building Instance Classification Using Street View Images
The proposed method is based on Convolutional Neural Networks (CNNs) which classify facade structures from street view images, such as Google StreetView, in addition to remote sensing images which usually only show roof structures.
Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNN
In this letter, we propose a pseudo-siamese convolutional neural network (CNN) architecture that enables to solve the task of identifying corresponding patches in very-high-resolution (VHR) optical and synthetic aperture radar (SAR) remote sensing imagery.
Deep learning in remote sensing: a review
In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with.
