# PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval

arXiv: https://arxiv.org/abs/1706.03424 · HTML: https://mlsanity.com/paper/1706.03424/

## Key facts

- Cited by 97 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2017.

## Built on by (most cited first, 30 of 97)

- [RemoteCLIP: A Vision Language Foundation Model for Remote Sensing](https://mlsanity.com/paper/2306.11029/) (2023)
- [Towards Geospatial Foundation Models via Continual Pretraining](https://mlsanity.com/paper/2302.04476/) (2023)
- [EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues](https://mlsanity.com/paper/2412.15190/) (2024)
- [TorchGeo: Deep Learning With Geospatial Data](https://mlsanity.com/paper/2111.08872/) (2021)
- [GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial Tasks](https://mlsanity.com/paper/2411.19325/) (2024)
- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data](https://mlsanity.com/paper/2401.17600/) (2024)
- [Towards Vision-Language Geo-Foundation Model: A Survey](https://mlsanity.com/paper/2406.09385/) (2024)
- [TOV: The Original Vision Model for Optical Remote Sensing Image Understanding via Self-supervised Learning](https://mlsanity.com/paper/2204.04716/) (2022)
- [Upcycling Models under Domain and Category Shift](https://mlsanity.com/paper/2303.07110/) (2023)
- [A Practitioner's Guide to Continual Multimodal Pretraining](https://mlsanity.com/paper/2408.14471/) (2024)
- [Domain-Controlled Prompt Learning](https://mlsanity.com/paper/2310.07730/) (2023)
- [RingMo-Agent: A Unified Remote Sensing Foundation Model for Multi-Platform and Multi-Modal Reasoning](https://mlsanity.com/paper/2507.20776/) (2025)
- [A Survey on Remote Sensing Foundation Models: From Vision to Multimodality](https://mlsanity.com/paper/2503.22081/) (2025)
- [Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification](https://mlsanity.com/paper/2207.07189/) (2022)
- [Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives](https://mlsanity.com/paper/2505.14361/) (2025)
- [Domain Prompt Learning with Quaternion Networks](https://mlsanity.com/paper/2312.08878/) (2023)
- [A Comprehensive Survey on Composed Image Retrieval](https://mlsanity.com/paper/2502.18495/) (2025)
- [SATIN: A Multi-Task Metadataset for Classifying Satellite Imagery using Vision-Language Models](https://mlsanity.com/paper/2304.11619/) (2023)
- [Composed Image Retrieval for Remote Sensing](https://mlsanity.com/paper/2405.15587/) (2024)
- [Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2403.01966/) (2024)
- [ImageNet-trained CNNs are not biased towards texture: Revisiting feature reliance through controlled suppression](https://mlsanity.com/paper/2509.20234/) (2025)
- [Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study](https://mlsanity.com/paper/2110.02580/) (2021)
- [Knowledge-aware Text-Image Retrieval for Remote Sensing Images](https://mlsanity.com/paper/2405.03373/) (2024)
- [Cross-View Meets Diffusion: Aerial Image Synthesis with Geometry and Text Guidance](https://mlsanity.com/paper/2408.04224/) (2024)
- [Aerial Scene Parsing: From Tile-level Scene Classification to Pixel-wise Semantic Labeling](https://mlsanity.com/paper/2201.01953/) (2022)
- [Enhancing Remote Sensing Vision-Language Models for Zero-Shot Scene Classification](https://mlsanity.com/paper/2409.00698/) (2024)
- [Onboard Satellite Image Classification for Earth Observation: A Comparative Study of ViT Models](https://mlsanity.com/paper/2409.03901/) (2024)
- [Advancing Image Super-resolution Techniques in Remote Sensing: A Comprehensive Survey](https://mlsanity.com/paper/2505.23248/) (2025)
- [AiTLAS: Artificial Intelligence Toolbox for Earth Observation](https://mlsanity.com/paper/2201.08789/) (2022)

## Abstract (arXiv)

Remote sensing image retrieval(RSIR), which aims to efficiently retrieve data of interest from large collections of remote sensing data, is a fundamental task in remote sensing. Over the past several decades, there has been significant effort to extract powerful feature representations for this task since the retrieval performance depends on the representative strength of the features. Benchmark datasets are also critical for developing, evaluating, and comparing RSIR approaches. Current benchmark datasets are deficient in that 1) they were originally collected for land use/land cover classification and not image retrieval, 2) they are relatively small in terms of the number of classes as well the number of sample images per class, and 3) the retrieval performance has saturated. These limitations have severely restricted the development of novel feature representations for RSIR, particularly the recent deep-learning based features which require large amounts of training data. We therefore present in this paper, a new large-scale remote sensing dataset termed "PatternNet" that was collected specifically for RSIR. PatternNet was collected from high-resolution imagery and contains 38 classes with 800 images per class. We also provide a thorough review of RSIR approaches ranging from traditional handcrafted feature based methods to recent deep learning based ones. We evaluate over 35 methods to establish extensive baseline results for future RSIR research using the PatternNet benchmark.