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TIRAuxCloud: A Thermal Infrared Dataset for Day and Night Cloud Detection

Alexis Apostolakis, Vasileios Botsos, Niklas Wölki, Andrea Spichtinger, Nikolaos Ioannis Bountos, Ioannis Papoutsis, Panayiotis Tsanakas

Abstract

Clouds are a major obstacle in Earth observation, limiting the usability and reliability of critical remote sensing applications such as fire disaster response, urban heat island monitoring, and snow and ice cover mapping. Therefore, the ability to detect clouds 24/7 is of paramount importance. While visible and near-infrared bands are effective for daytime cloud detection, their dependence on solar illumination makes them unsuitable for nighttime monitoring. In contrast, thermal infrared (TIR) imagery plays a crucial role in detecting clouds at night, when sunlight is absent. Due to their generally lower temperatures, clouds emit distinct thermal signatures that are detectable in TIR bands. Despite this, accurate nighttime cloud detection remains challenging due to limited spectral information and the typically lower spatial resolution of TIR imagery. To address these challenges, we present TIRAuxCloud, a multi-modal dataset centered around thermal spectral data to facilitate cloud segmentation under both daytime and nighttime conditions. The dataset comprises a unique combination of multispectral data (TIR, optical, and near-infrared bands) from Landsat and VIIRS, aligned with auxiliary information layers. Elevation, land cover, meteorological variables, and cloud-free reference images are included to help reduce surface-cloud ambiguity and cloud formation uncertainty. To overcome the scarcity of manual cloud labels, we include a large set of samples with automated cloud masks and a smaller manually annotated subset to further evaluate and improve models. Comprehensive benchmarks are presented to establish performance baselines through supervised and transfer learning, demonstrating the dataset's value in advancing the development of innovative methods for day and night time cloud detection.

TIRAuxCloud: A Thermal Infrared Dataset for Day and Night Cloud Detection

Abstract

Clouds are a major obstacle in Earth observation, limiting the usability and reliability of critical remote sensing applications such as fire disaster response, urban heat island monitoring, and snow and ice cover mapping. Therefore, the ability to detect clouds 24/7 is of paramount importance. While visible and near-infrared bands are effective for daytime cloud detection, their dependence on solar illumination makes them unsuitable for nighttime monitoring. In contrast, thermal infrared (TIR) imagery plays a crucial role in detecting clouds at night, when sunlight is absent. Due to their generally lower temperatures, clouds emit distinct thermal signatures that are detectable in TIR bands. Despite this, accurate nighttime cloud detection remains challenging due to limited spectral information and the typically lower spatial resolution of TIR imagery. To address these challenges, we present TIRAuxCloud, a multi-modal dataset centered around thermal spectral data to facilitate cloud segmentation under both daytime and nighttime conditions. The dataset comprises a unique combination of multispectral data (TIR, optical, and near-infrared bands) from Landsat and VIIRS, aligned with auxiliary information layers. Elevation, land cover, meteorological variables, and cloud-free reference images are included to help reduce surface-cloud ambiguity and cloud formation uncertainty. To overcome the scarcity of manual cloud labels, we include a large set of samples with automated cloud masks and a smaller manually annotated subset to further evaluate and improve models. Comprehensive benchmarks are presented to establish performance baselines through supervised and transfer learning, demonstrating the dataset's value in advancing the development of innovative methods for day and night time cloud detection.
Paper Structure (12 sections, 5 figures, 4 tables)

This paper contains 12 sections, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Dataset Description
  4. Landsat Main Dataset
  5. MA Landsat Dataset
  6. VIIRS Dataset
  7. Benchmark and Results
  8. Feature Combination Benchmarks
  9. Visual examples
  10. Cross-Domain transferability
  11. Discussion
  12. Conclusion

Figures (5)

  • Figure 1: Global distribution of sampled Landsat scenes used for training, colored by elevation. Each one contains all the cloudy bands mentioned in \ref{['sec:dd']} alongside their clear counterparts and the auxiliary bands. Cloudy bands panel displays Cloudy Radiance B10/B11 mean, while the Clear bands panel shows Clear Radiance B10/B11 mean.
  • Figure 2: Example bands from a 256×256 Landsat 8 patch.
  • Figure 3: Distribution of Landsat and Manually Annotated Landsat patches across Köppen–Geiger climate zones beck2018koppen.
  • Figure 4: Temporal distribution of patches across months for VIIRS, Manually Annotated Landsat, and Landsat subsets.
  • Figure 5: Examples of inference where auxiliary input contributed to the improvement of accuracy of the model. (a) The model trained only on Cloudy TIR predicts a false positive because the ground in that area exhibited lower than usual thermal values and real clouds were present nearby, causing the region to resemble a cloud. Clear TIR helped resolve this uncertainty. (b) The inclusion of the DEM helps resolve the common infrared based confusion between clouds and high altitude cold terrain (ice/snow), evident in the TIR-only prediction that mistakenly labels part of a mountain as cloud. (c) and (d) The combination of low precipitation and high surface pressure (conditions that suppress deep moist convection and the formation of thick clouds) helps the model more effectively distinguish thin cloud layers from denser cloud cover.