A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning

Deep Residual Learning for Image Recognition

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You Only Look Once: Unified, Real-Time Object Detection

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Going deeper with convolutions

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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint

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Caffe: Convolutional Architecture for Fast Feature Embedding

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Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

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Deep Residual Learning for Image Recognition

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Online Object Tracking: A Benchmark

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Single-Image Crowd Counting via Multi-Column Convolutional Neural Network

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Spatial Cognition-Driven Deep Learning for Car Detection in Unmanned Aerial Vehicle Imagery

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Region-Aware Quantum Network for Crowd Counting

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Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data

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Vision-Language Modeling Meets Remote Sensing: Models, datasets, and perspectives

Weng, X., Pang, C., & Xia, G.-S. (2025) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2025.3572702 · Cited by 47

YOLO-RTUAV: Towards Real-Time Vehicle Detection through Aerial Images with Low-Cost Edge Devices

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Distillation Remote Sensing Object Counting via Multi-Scale Context Feature Aggregation

Duan, Z., Wang, S., Di, H., & Deng, J. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3125249 · Cited by 34

Spatiotemporal modeling for crowd counting in videos

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An accurate car counting in aerial images based on convolutional neural networks

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Vegetation Extraction from UAV-based Aerial Images through Deep Learning

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Vehicle and Vessel Detection on Satellite Imagery: A Comparative Study on Single-Shot Detectors

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Vehicle Detection in Overhead Satellite Images Using a One-Stage Object Detection Model

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Deep Residual Dual-Attention Network for Super-Resolution Reconstruction of Remote Sensing Images

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Vehicle Detection Based on Drone Images with the Improved Faster R-CNN

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Structure-Guided Feature Transform Hybrid Residual Network for Remote Sensing Object Detection

Li, J., Zhang, H., Song, R., Xie, W., Li, Y., & Du, Q. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3103964 · Cited by 30

Information System Design for Deep Learning Based Plant Counting Automation

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SHAP-Based Interpretable Object Detection Method for Satellite Imagery

Kawauchi, H., & Fuse, T. (2022) · Remote Sensing · doi:10.3390/rs14091970 · Cited by 30

Traffic congestion-aware graph-based vehicle rerouting framework from aerial imagery

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Counting Dense Objects in Remote Sensing Images

Gao, G., Liu, Q., & Wang, Y. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053690 · Cited by 30

Improving Object Counting with Heatmap RegulationPreprint

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A large scale training sample database system for intelligent interpretation of remote sensing imagery

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Faster RER-CNN: application to the detection of vehicles in aerial imagesPreprint

du Terrail, J. O., & Jurie, F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.07628 · Cited by 30

Detection of Parking Cars in Stereo Satellite Images

Zambanini, S., Loghin, A.-M., Pfeifer, N., Soley, E. M., & Sablatnig, R. (2020) · Remote Sensing · doi:10.3390/rs12132170 · Cited by 31

Unlocking the capabilities of explainable few-shot learning in remote sensing

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Multi-Angle Parking Detection System using Mask R-CNN

Agrawal, T. L., & Urolagin, S. (2020) · International Conference Big Data Engineering and Technology · doi:10.1145/3378904.3378914 · Cited by 26

Vehicle Classification Using Deep Feature Fusion and Genetic Algorithms

Alghamdi, A., Saeed, A., Kamran, M., Mursi, K. T., & ALMUKADI, W. S. (2023) · Electronics · doi:10.3390/electronics12020280 · Cited by 26

Super resolution-assisted deep aerial vehicle detection

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Counting and locating high-density objects using convolutional neural network

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Class-Agnostic Object Counting Robust to Intraclass Diversity

Gong, S., Zhang, S., Yang, J., Dai, D., & Schiele, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19827-4_23 · Cited by 25

Representation Learning by Learning to CountPreprint

Noroozi, M., Pirsiavash, H., & Favaro, P. (2017) · arXiv (Cornell University) · doi:10.13016/m2qaoo-thod · Cited by 25

Truck traffic monitoring with satellite images

Kaack, L. H., Chen, G. H., & Morgan, M. G. (2019) · Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies · doi:10.1145/3314344.3332480 · Cited by 24

Zero-Shot Object Counting with Good Exemplars

Zhu, H., Yuan, J., Yang, Z., Guo, Y., Wang, Z., Zhong, X., & He, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72652-1_22 · Cited by 28

Target-Guided Feature Super-Resolution for Vehicle Detection in Remote Sensing Images

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Informed Region Selection for Efficient UAV-based Object Detectors: Altitude-aware Vehicle Detection with CyCAR Dataset

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Guided Attention Network for Object Detection and Counting on DronesPreprint

Cai, Y., Du, D., Zhang, L., Wen, L., Wang, W., Wu, Y., & Lyu, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.11307 · Cited by 24

TorchGeo: Deep Learning With Geospatial Data

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Two-Pass Bilateral Smooth Filtering for Remote Sensing Imagery

Chen, B., Cheng, H.-Y., Tseng, Y.-S., & Yin, J.-L. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2020.3048488 · Cited by 22

Image-Based Crowd Stability Analysis Using Improved Multi-Column Convolutional Neural Network

Zhao, R., Dong, D., Wang, Y., Li, C., Ma, Y., & Enriquez, V. F. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3054376 · Cited by 22

Vehicle Count System based on Time Interval Image Capture Method and Deep Learning Mask R-CNN

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FSSCaps-DetCountNet: fuzzy soft sets and CapsNet-based detection and counting network for monitoring animals from aerial images

Sundaram, D. M., & Loganathan, A. (2020) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.14.026521 · Cited by 20

Development of a Car-Free Street Mapping Model Using an Integrated System with Unmanned Aerial Vehicles, Aerial Mapping Cameras, and a Deep Learning Algorithm

Lee, S., Kim, S., & Moon, S. (2022) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0001013 · Cited by 20

Object Counting in Remote Sensing via Triple Attention and Scale-Aware Network

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A Deep Learning Framework for Video-Based Vehicle Counting

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AI Enabling Technologies: A SurveyPreprint

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On the use of deep neural networks for the detection of small vehicles in ortho-images

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UnityShip: A Large-Scale Synthetic Dataset for Ship Recognition in Aerial Images

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Hierarchical Kernel Interaction Network for Remote Sensing Object Counting

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Bidirectional Long Short-Term Memory Network for Vehicle Behavior Recognition

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DOPNet: Dense Object Prediction Network for Multiclass Object Counting and Localization in Remote Sensing Images

Cui, M., Ding, G., Yang, D., & Chen, Z. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3349702 · Cited by 18

Explainable AI for Earth observation: current methods, open challenges, and opportunities

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EarthNets: Empowering artificial intelligence for Earth observation

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MobileNet‐SSD MicroScope using adaptive error correction algorithm: real‐time detection of license plates on mobile devices

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Small Object Detection Based on Deep Learning

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Dronecaps: Recognition Of Human Actions In Drone Videos Using Capsule Networks With Binary Volume Comparisons

Algamdi, A. M., Sánchez, V., & Li, C. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9190864 · Cited by 19

CODAN: Counting-driven Attention Network for Vehicle Detection in Congested Scenes

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Dense-TNT: Efficient Vehicle Type Classification Neural Network Using Satellite Imagery

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A review for sample datasets of remote sensing imagery

Feng, Q., Chen, B., Guoqing, L. I., Yao, X., Bingbo, G. A. O., & Zhang, L. (2022) · National Remote Sensing Bulletin · doi:10.11834/jrs.20221162 · Cited by 23

Privacy-aware crowd counting by decentralized learning with parallel transformers

Chen, J., Gao, M., Li, Q., Guo, X., Wang, J., Cheng, J., & Xing, X. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101167 · Cited by 18

Vision-Based Accident Anticipation and Detection Using Deep Learning

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Deep Learning Algorithms for Vehicle Detection on UAV Platforms: First Investigations on the Effects of Synthetic Training

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Vehicle Detection and Classification in Aerial Imagery

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Research on Vehicle Detection Based on Faster R-CNN for UAV Images

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Citywide reconstruction of cross-sectional traffic flow from moving camera videos

Kumar, A., Kashiyama, T., Maeda, H., & Sekimoto, Y. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671751 · Cited by 18

Generalized Characteristic Function Loss for Crowd Analysis in the Frequency Domain

Shu, W., Wan, J., & Chan, A. B. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3336196 · Cited by 15

Object Counting via Group and Graph Attention Network

Guo, X., Gao, M., Zou, G., Bruno, A., Chehri, A., & Jeon, G. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3336894 · Cited by 21

Balanced Density Regression Network for Remote Sensing Object Counting

Guo, H., Gao, J., & Yuan, Y. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3402271 · Cited by 16

REMODEL: Rethinking Deep CNN Models to Detect and Count on a NeuroSynaptic System

Shukla, R., Lipasti, M. H., Essen, B. V., Moody, A. T., & Maruyama, N. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00004 · Cited by 16

BETTER GENERIC OBJECTS COUNTING WHEN ASKING QUESTIONS TO IMAGES: A MULTITASK APPROACH FOR REMOTE SENSING VISUAL QUESTION ANSWERING

Lobry, S., Marcos, D., Kellenberger, B., & Tuia, D. (2020) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-v-2-2020-1021-2020 · Cited by 17

Artificial and beneficial – Exploiting artificial images for aerial vehicle detection

Weber, I., Bongartz, J., & Roscher, R. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.02.015 · Cited by 17

Congested Crowd Counting via Adaptive Multi-Scale Context Learning

Zhang, Y., Zhao, H., Duan, Z., Huang, L., Deng, J., & Zhang, Q. (2021) · Sensors · doi:10.3390/s21113777 · Cited by 17

Mutual-Feed Learning for Super-Resolution and Object Detection in Degraded Aerial Imagery

Yang, J., Fu, K., Wu, Y., Diao, W., Dai, W., & Sun, X. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3198083 · Cited by 16

Extracting High-Precision Vehicle Motion Data from Unmanned Aerial Vehicle Video Captured under Various Weather Conditions

Li, X., & Wu, J. (2022) · Remote Sensing · doi:10.3390/rs14215513 · Cited by 17

VehSat: a Large-Scale Dataset for Vehicle Detection in Satellite Images

Drouyer, S. (2020) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss39084.2020.9323289 · Cited by 16

Enhancing real human detection and people counting using YOLOv8

Shyaa, T. A., & Hashim, A. A. (2024) · BIO Web of Conferences · doi:10.1051/bioconf/20249700061 · Cited by 15

Vicinal Counting Networks

Ranjan, V., & Hoai, M. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00467 · Cited by 15

Power to the people: Applying citizen science and computer vision to home mapping for rural energy access

Leonard, A., Wheeler, S., & McCulloch, M. D. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.102748 · Cited by 15

DiRS: On Creating Benchmark Datasets for Remote Sensing Image InterpretationPreprint

Long, Y., Xia, G.-S., Li, S., Wen, Y., Yang, M. Y., Zhu, X. X., Zhang, L., & Li, D. (2020) · arXiv (Cornell University) · Cited by 15

City Scale Traffic Monitoring Using WorldView Satellite Imagery and Deep Learning: A Case Study of Barcelona

Sheehan, A., Beddows, A. V., Green, D. C., & Beevers, S. D. (2023) · Remote Sensing · doi:10.3390/rs15245709 · Cited by 17

CSTrans: Correlation-guided Self-Activation Transformer for Counting Everything

Gao, B.-B., & Huang, Z. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110556 · Cited by 14

LDNet: High Accuracy Fish Counting Framework using Limited training samples with Density map generation Network

Li, X., Zhuang, Y., You, B., Wang, Z., Zhao, J., Gao, Y., & Xiao, D. (2024) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2024.102143 · Cited by 14

Identifying Streetscape Features Using VHR Imagery and Deep Learning Applications

Verma, D., Mumm, O., & Carlow, V. M. (2021) · Remote Sensing · doi:10.3390/rs13173363 · Cited by 14

People counting and pedestrian flow statistics based on convolutional neural network and recurrent neural network

Zhu, J., Feng, F., & Shen, B. (2018) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac.2018.8406516 · Cited by 14

From Pixel to Peril: Investigating Adversarial Attacks on Aerial Imagery Through Comprehensive Review and Prospective Trajectories

Kazmi, S. M. K. A., Aafaq, N., Khan, M. A., Khalil, M., & Saleem, A. (2023) · IEEE Access · doi:10.1109/access.2023.3299878 · Cited by 13

Power Supply Technologies for Drones and Machine Vision Applications: A Comparative Analysis and Future Trends

Pekias, A., Maraslidis, G. S., Tsipouras, M. G., Koumboulis, F. N., & Fragulis, G. F. (2023) · Telecom · doi:10.3390/telecom4030024 · Cited by 14

Managing the risks against carbon neutralization for green maritime transport

Pala, M. Ö., Kazançoğlu, Y., Karamperidis, S., & Ram, M. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.142478 · Cited by 13

Highway Traffic Monitoring on Medium Resolution Satellite Images

Drouyer, S., & de Franchis, C. (2019) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2019.8899777 · Cited by 13

LOVD: Land Vehicle Detection in Complex Scenes of Optical Remote Sensing Image

Yan, P., Liu, X., Wang, F., Yue, C., & Wang, X. Y. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3133109 · Cited by 13

Accelerating Deep Learning Applications in SpacePreprint

Lofqvist, M., & Cano, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.11089 · Cited by 13

Object counting in remote sensing via selective spatial‐frequency pyramid network

Chen, J., Gao, M., Guo, X., Zhai, W., Li, Q., & Jeon, G. (2023) · Software Practice and Experience · doi:10.1002/spe.3287 · Cited by 12

VrsNet - density map prediction network for individual tree detection and counting from UAV images

Luo, T., Gao, W., Belotserkovsky, A., Nedzved, A., Deng, W., Ye, Q., Fu, L., Qiao, C., Ma, W., & Xu, S. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.103923 · Cited by 13

Analysis of the Influence of De-hazing Methods on Vehicle Detection in Aerial Images

Nguyen, K. T. T. M., Nguyen, P., Bui, D. C., Tran, M. T., & Vo, N. D. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.01306100 · Cited by 12

Vehicle Counting in Very Low-Resolution Aerial Images via Cross-Resolution Spatial Consistency and Intraresolution Time Continuity

Zhao, Q., Xiao, J., Wang, Z., Ma, X., Wang, M., & Satoh, S. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3174815 · Cited by 12

Drone-Based Car Counting via Density Map Learning

Huang, J., Ding, G., Guo, Y., Yang, D., Wang, S., Wang, T., & Zhang, Y. (2020) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip49819.2020.9301785 · Cited by 12

Object Counting for Remote-Sensing Images via Adaptive Density Map-Assisted Learning

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Object Detection in Drone Imagery via Sample Balance Strategies and Local Feature Enhancement

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