Scene Classification
144 papers with code • 2 benchmarks • 23 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
RSICD
Places365
AID
Million-AID
DCASE 2016
Most implemented papers
Remote Sensing Image Scene Classification: Benchmark and State of the Art
techmn/satmae_pp
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During the past years, significant efforts have been made to develop various datasets or present a variety of approaches for scene classification from remote sensing images.
Spatial Information Considered Network for Scene Classification
MindCode-4/code-9
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IEEE Geoscience and Remote Sensing Letters 2020
Besides, we present an RSI scene classification dataset named as CSU-RSISC10 dataset to preserve the spatial information between scenes in a new way of organization.
Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification
kainoj/colnet
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ACM Transactions on Graphics 2016
We present a novel technique to automatically colorize grayscale images that combines both global priors and local image features.
Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization
The proposed methods are highly modular, readily plugged into existing deep CNNs.
The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification
To this end, we analyse the receptive field (RF) of these CNNs and demonstrate the importance of the RF to the generalization capability of the models.
Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions
louisccc/av_av
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Finally, we demonstrate that the use of spatial and temporal attention layers improves our model's performance by 2. 7% and 0. 7% respectively, and increases its explainability.
Generalized Scene Classification from Small-Scale Datasets with Multi-Task Learning
pwc-1/Paper-9
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IEEE Transactions on Geoscience and Remote Sensing 2021
Concretely, the MTLN consists of a shared branch for all tasks and multiple task-specific branches with each for one task.
Vision-Language Models in Remote Sensing: Current Progress and Future Trends
lzw-lzw/awesome-remote-sensing-vision-language-models
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Existing AI-related research in remote sensing primarily focuses on visual understanding tasks while neglecting the semantic understanding of the objects and their relationships.
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.
Knowledge Guided Disambiguation for Large-Scale Scene Classification with Multi-Resolution CNNs
Convolutional Neural Networks (CNNs) have made remarkable progress on scene recognition, partially due to these recent large-scale scene datasets, such as the Places and Places2.
