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Works391 from public data
- Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources3,334
The challenges of using deep learning for remote sensing data analysis are analyzed, the recent advances are reviewed, and resources are provided to make deep learning in remote sensing ridiculously simple to start with.
- DOTA: A Large-Scale Dataset for Object Detection in Aerial Images3,266
A large-scale Dataset for Object deTection in Aerial images (DOTA) is introduced and state-of-the-art object detection algorithms on DOTA are evaluated, demonstrating that DOTA well represents real Earth Vision applications and are quite challenging.
- AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification2,351
The Aerial Image data set (AID), a large-scale data set for aerial scene classification, is described to advance the state of the arts in scene classification of remote sensing images and can be served as the baseline results on this benchmark.
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- Align Deep Features for Oriented Object Detection1,221
A single-shot alignment network (S2A-Net) consisting of two modules: a feature alignment module (FAM) and an oriented detection module (ODM) that can achieve the state-of-the-art performance on two commonly used aerial objects’ data sets while keeping high efficiency.
- Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery1,195
This paper proposes two scenarios for generating image features via extracting CNN features from different layers and reveals that the features from pre-trained CNNs generalize well to HRRS datasets and are more expressive than the low- and mid-level features.
- Land-cover classification with high-resolution remote sensing images using transferable deep models1,022
The main idea is to rely on deep neural networks for presenting the contextual information contained in different types of land-covers and propose a pseudo-labeling and sample selection scheme for improving the transferability of deep models.
- Gliding Vertex on the Horizontal Bounding Box for Multi-Oriented Object Detection1,013
An obliquity factor based on area ratio between the object and its horizontal bounding box, guiding the selection of horizontal or oriented detection for each object is introduced, and five extra target variables are added to the regression head of faster R-CNN, which requires ignorable extra computation time.
- Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities994
This article provides a systematic survey of deep learning methods for remote sensing image scene classification by covering more than 160 papers and discusses the main challenges of remote sensing images classification and survey.
- ReDet: A Rotation-equivariant Detector for Aerial Object Detection935
A Rotation-equivariant Detector (ReDet) is proposed, which explicitly encodes rotation equivariance and rotation invariance and incorporates rotation- equivariant networks into the detector to extract rotation-Equivariant features, which can accurately predict the orientation and lead to a huge reduction of model size.
- Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges714
The expanded large-scale DOTA dataset, the extensive baselines, the code library and the challenges can facilitate the designs of robust algorithms and reproducible research on the problem of object detection in aerial images.
- Rotation-Sensitive Regression for Oriented Scene Text Detection563
The proposed method named Rotation-sensitive Regression Detector (RRD) achieves state-of-the-art performance on several oriented scene text benchmark datasets, including ICDAR 2015, MSRA-TD500, RCTW-17, and COCO-Text, and achieves a significant improvement on a ship collection dataset, demonstrating its generality on oriented object detection.
- UAVid: A semantic segmentation dataset for UAV imagery514
The UAVid dataset is introduced, a new high-resolution UAV semantic segmentation dataset as a complement, which brings new challenges, including large scale variation, moving object recognition and temporal consistency preservation.
- Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark461
A new evaluation metric dubbed Normalized Wasserstein Distance (NWD) and a new RanKing-based Assigning (RKA) strategy for tiny object detection are proposed, which can be easily embedded into all kinds of anchor-based detectors to replace the standard IoU threshold-based one.
- A survey on vision-based UAV navigation447
This article is to present a comprehensive literature review of the vision-based methods for UAV navigation, specifically on visual localization and mapping, obstacle avoidance and path planning, which compose the essential parts of visual navigation.
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- STRUCTURAL HIGH-RESOLUTION SATELLITE IMAGE INDEXING352
A satellite image indexing method relying on topographic maps and a shape-based image indexed scheme that is robust to changes in scale, orientation and contrast is suggested.
- An Empirical Study of Remote Sensing Pretraining321
Empirical study shows that RSP can help deliver distinctive performances in scene recognition tasks and in perceiving RS related semantics such as “Bridge” and “Airplane”, and finds that, although RSP mitigates the data discrepancies of traditional ImageNet pretraining on RS images, it may still suffer from task discrepancies, where downstream tasks require different representations from scene Recognition tasks.
- Decoupling Zero-Shot Semantic Segmentation319
A simple and effective zero-shot semantic segmentation model, called ZegFormer, is proposed, which outperforms the previous methods on ZS3 standard benchmarks by large margins, e.g., 22 points on the PAS-CAL VOC and 3 points onThe COCO-Stuff in terms of mIoU for unseen classes.
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- On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID292
The current challenges of developing intelligent algorithms for RS image interpretation with bibliometric investigations are analyzed and the general guidances on creating benchmark datasets in efficient manners are presented.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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