mlsanityData to 14 Oct 2026

Deep Learning for Spatio-Temporal Fusion in Land Surface Temperature Estimation: A Comprehensive Survey, Experimental Analysis, and Future Trends

arXiv 2412.16631 ↗First posted 21 Dec 2024cs.LG cs.AI cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

Land Surface Temperature (LST) plays a key role in climate monitoring, urban heat assessment, and land-atmosphere interactions. However, current thermal infrared satellite sensors cannot simultaneously achieve high spatial and temporal resolution. Spatio-temporal fusion (STF) techniques address this limitation by combining complementary satellite data, one with high spatial but low temporal resolution, and another with high temporal but low spatial resolution. Existing STF techniques, from classical models to modern deep learning (DL) architectures, were primarily developed for surface reflectance (SR). Their application to thermal data remains limited and often overlooks LST-specific spatial and temporal variability. This study provides a focused review of DL-based STF methods for LST. We present a formal mathematical definition of the thermal fusion task, propose a refined taxonomy of relevant DL methods, and analyze the modifications required when adapting SR-oriented models to LST. To support reproducibility and benchmarking, we introduce a new dataset comprising 51 Terra MODIS-Landsat LST pairs from 2013 to 2024, and evaluate representative models to explore their behavior on thermal data. The analysis highlights performance gaps, architecture sensitivities, and open research challenges. The dataset and accompanying resources are publicly available at https://github.com/Sofianebouaziz1/STF-LST.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 40 of its 216 references that are in the map. First posted on arXiv 21 Dec 2024.

Builds on

40 of 216 references.

References · 40

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  5. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  6. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  7. 2020Training data-efficient image transformers & distillation through attentioncited by 4,586
  8. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  9. 2014Conditional Generative Adversarial Netscited by 2,162
  10. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  11. 2021Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionscited by 1,962
  12. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,908
28 more
  1. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,636
  2. 2018Group Normalizationcited by 1,524
  3. 2016Instance Normalization: The Missing Ingredient for Fast Stylizationcited by 1,198
  4. 2020Pre-Trained Image Processing Transformercited by 893
  5. 2016InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netscited by 860
  6. 2021Transformer in Transformercited by 633
  7. 2021Twins: Revisiting the Design of Spatial Attention in Vision Transformerscited by 565
  8. 2022A Systematic Evaluation of Large Language Models of Codecited by 363
  9. 2016Adversarial Feature Learningcited by 350
  10. 2016Generative Visual Manipulation on the Natural Image Manifoldcited by 348
  11. 2016Adversarially Learned Inferencecited by 216
  12. 2015Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networkscited by 214
  13. 2021Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encodingcited by 207
  14. 2017Image De-raining Using a Conditional Generative Adversarial Networkcited by 197
  15. 2015Variable Rate Image Compression with Recurrent Neural Networkscited by 173
  16. 2021VOLO: Vision Outlooker for Visual Recognitioncited by 165
  17. 2017Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networkscited by 107
  18. 2023Vision-Language Models in Remote Sensing: Current Progress and Future Trendscited by 93
  19. 2020Normalization Techniques in Training DNNs: Methodology, Analysis and Applicationcited by 90
  20. 2024Image Fusion via Vision-Language Modelcited by 40
  21. 2017A Hybrid Convolutional Variational Autoencoder for Text Generationcited by 33
  22. 2018Infrared and Visible Image Fusion using a Deep Learning Frameworkcited by 33
  23. 2018Differentiable Learning-to-Normalize via Switchable Normalizationcited by 33
  24. 2018Deep learning based cloud detection for medium and high resolution remote sensing images of different sensorscited by 20
  25. 2018Infrared and Visible Image Fusion with ResNet and zero-phase component analysiscited by 16
  26. 2017SRPGAN: Perceptual Generative Adversarial Network for Single Image Super Resolutioncited by 5
  27. 2018Spatio-temporal Stacked LSTM for Temperature Prediction in Weather Forecastingcited by 3
  28. 2023An evaluation of pre-trained models for feature extraction in image classificationcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025FuseTen: A Generative Model for Daily 10 m Land Surface Temperature Estimation from Spatio-Temporal Satellite Observationscited by 1
  2. 2025WGAST: Weakly-Supervised Generative Network for Daily 10 m Land Surface Temperature Estimation via Spatio-Temporal Fusioncited by 1
  3. 2025Four decades of circumpolar super-resolved satellite land surface temperature datacited by 0
  4. 2026Uncertainty-Aware Test-Time Adaptation for Cross-Region Spatio-Temporal Fusion of Land Surface Temperaturecited by 0