Scene Classification
121 papers with code • 2 benchmarks • 21 datasets
Scene Classification is a task in which scenes from photographs are categorically classified. Unlike object classification, which focuses on classifying prominent objects in the foreground, Scene Classification uses the layout of objects within the scene, in addition to the ambient context, for classification.
Source: Scene classification with Convolutional Neural Networks
Benchmarks
These leaderboards are used to track progress in Scene Classification
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Datasets
RESISC45
BigEarthNet
Places365
RSICD
DCASE 2016
AID
Million-AID
Latest papers
Bridging Remote Sensors with Multisensor Geospatial Foundation Models
boranhan/geospatial_foundation_models
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A key discovery of our research is that representations derived from natural images are not always compatible with the distinct characteristics of geospatial remote sensors, underscoring the limitations of existing representations in this field.
RSMamba: Remote Sensing Image Classification with State Space Model
KyanChen/RSMamba
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Remote sensing image classification forms the foundation of various understanding tasks, serving a crucial function in remote sensing image interpretation.
MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining
However, transferring the pretrained models to downstream tasks may encounter task discrepancy due to their formulation of pretraining as image classification or object discrimination tasks.
Comparing Importance Sampling Based Methods for Mitigating the Effect of Class Imbalance
richardzhu123/514-class-imbalance
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Specifically, we compare the effect of these techniques on the performance of two encoders on an impactful satellite imagery dataset, Planet's Amazon Rainforest dataset, in preparation for another work.
Efficient Multi-Resolution Fusion for Remote Sensing Data with Label Uncertainty
Previously, we developed a Multiple Instance Multi-Resolution Fusion (MIMRF) framework that addresses label uncertainty for fusion, but it can be slow to train due to the large search space for the fuzzy measures used to integrate sensor data sources.
Description on IEEE ICME 2024 Grand Challenge: Semi-supervised Acoustic Scene Classification under Domain Shift
In addition, considering the abundance of unlabeled acoustic scene data in the real world, it is important to study the possible ways to utilize these unlabelled data.
Deep Semantic-Visual Alignment for Zero-Shot Remote Sensing Image Scene Classification
Besides, pioneer ZSL models use convolutional neural networks pre-trained on ImageNet, which focus on the main objects appearing in each image, neglecting the background context that also matters in RS scene classification.
Generic Knowledge Boosted Pre-training For Remote Sensing Images
Deep learning models are essential for scene classification, change detection, land cover segmentation, and other remote sensing image understanding tasks.
SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing
Remote sensing imagery, despite its broad applications in helping achieve Sustainable Development Goals and tackle climate change, has not yet benefited from the recent advancements of versatile, task-agnostic vision language models (VLMs).
GeoChat: Grounded Large Vision-Language Model for Remote Sensing
Furthermore, the lack of domain-specific multimodal instruction following data as well as strong backbone models for RS make it hard for the models to align their behavior with user queries.
