| --- |
| license: cc-by-4.0 |
| task_categories: |
| - image-segmentation |
| - image-classification |
| tags: |
| - astronomy |
| - atmospheric-science |
| - infrared |
| - lwir |
| - thermal-imaging |
| - clouds |
| - remote-sensing |
| - fits |
| size_categories: |
| - 10K<n<100K |
| pretty_name: IRIS-CloudDeep LWIR sky/cloud images |
| --- |
| |
| # IRIS-CloudDeep |
|
|
| Ground-based long-wave infrared (LWIR) images of the night sky, with the binary ground-truth masks and clear/cloud labels behind [Sommer, Kabalan and Brunet (2025), *Atmos. Meas. Tech.* **18**, 2083–2101](https://doi.org/10.5194/amt-18-2083-2025). |
|
|
| An uncooled FLIR Tau2 microbolometer (640×512, 17 μm pitch, 8–14 μm band, 9 Hz) recorded two night-time campaigns in early 2023 at Prades-le-Lez, France (43°41′51″ N, 3°51′53″ E). A 60 mm f/1.25 lens gives a narrow imaging area of **10.4° × 8.3°**, about 58″ per pixel. The camera sits on the equatorial mount beside the StarDICE photometric telescope and watches the same line of sight, so the question the data answers is whether cloud is crossing the telescope's field. |
|
|
| The file counts match the published dataset: 4,445 segmentation images and 3,413 classification images, against the paper's 4445 and "3400". |
|
|
| Pixel values are raw 14-bit ADU with `TLINEAR` disabled, so they are uncalibrated detector counts rather than radiance or brightness temperature. Every loader in the reference code divides by `2**14`. **Warmer is brighter, and cloud is warmer than clear sky** — the dark patches in the images below are clear sky, not cloud. |
|
|
|  |
|
|
| **31,352 FITS files, ~18 GB.** |
|
|
| ## What is in it |
|
|
| | Path | Files | Size | Contents | |
| |---|---:|---:|---| |
| | `raw/DATASET_1/` | 18,748 | 12 GB | Complete campaign of 2023-01-23, 640×512 `int16` | |
| | `raw/DATASET_2/` | 4,746 | 3.0 GB | Complete campaign of 2023-03-30, same format | |
| | `segmentation/DS1_MASKED_SUBSET/` | 2,951 | 600 MB | 160×128 image **+ ground-truth mask**, from `DATASET_1` | |
| | `segmentation/DS2_MASKED_SUBSET/` | 1,494 | 304 MB | Same, from `DATASET_2` | |
| | `classification/CLOUD/` | 1,413 | 889 MB | Cloudy frames, label `1` | |
| | `classification/SIM_CLEAR/` | 2,000 | 1.3 GB | Synthetic clear-sky frames, label `0` | |
| | `manifests/` | 3 CSV | 1 MB | Provenance: every derived file → its source raw frame | |
|
|
| ### The two campaigns are not interchangeable |
|
|
| | | `DATASET_1` | `DATASET_2` | |
| |---|---|---| |
| | Date (UTC, from `DATE-OBS`) | 2023-01-23 22:01:54 → 23:04:29 | 2023-03-30 21:27:43 → 21:51:18 | |
| | Span / frames | 62.8 min / 18,748 | 23.7 min / 4,746 | |
| | Detector temperature (`FPA-TEMP`) | −1.0 … −0.2 °C | +11.2 … +11.5 °C | |
| | Ambient (`AMB-TEMP`) | −1.26 … +0.01 °C | +11.95 … +12.60 °C | |
| | Frame mean ADU | 1001 ± 155 | 1135 ± 106 | |
| | Within-frame σ (contrast) | 74 ADU | 25 ADU | |
|
|
| The January night was ~12 °C colder, and its frames carry roughly three times the internal contrast. The two ADU distributions barely overlap (centre panel below), so a model fitted on one campaign will not transfer to the other without per-frame normalisation. `DATASET_2`'s low contrast also means its Otsu-derived masks are separating a much weaker gradient than `DATASET_1`'s. |
|
|
| Constant across both campaigns: `GAIN='high'`, `TLINEAR='disabled'`, `SHU-MODE='user'`, `FFCMODE='external'`, `STACKED=1`, `XPMODE='CMOS 14 bits'`, `CAMERA='FLIR TAU2'`, `FOCAL=60`, `APERTURE=1.25`, `PXSIZE=17`. Flat-field correction runs about every 30 s against an external calibration source. |
|
|
| ## File formats |
|
|
| **`raw/` and `classification/`** — one HDU: |
|
|
| ``` |
| HDU 0 640 × 512 int16 (big-endian) raw ADU |
| ``` |
|
|
| Real-camera frames carry the full instrument state in the header: `DATE-OBS`, `FPA-TEMP`, `AMB-TEMP`, `BB-TEMP`, `GAIN`, `TLINEAR`, `SHU-MODE`, `SHU-TEMP`, `FFCMODE`, `FFC-TIME`, `FFCFRAME`, `XPMODE`, `STACKED`, `CAMERA`, `LENS`, `FOCAL`, `APERTURE`, `PXSIZE`, `LATOBS`, `LONGOBS`, `TARGET`, `OBSERVER`. Synthetic frames instead carry their generation parameters: `IMGTYPE='SIMCLEAR'`, `START`, `STOP`, `HORIZ`, `BIASLVL`, `READLVL`, `FPNLVL`, `SKYNOISE`, `BADPXCOL`, `NARCIS`, `NARINT`, `RADIUS`, `CENTER_X`, `CENTER_Y`, `SMOOTH`, `AUGMENT`, `INDEX`. |
|
|
| **`segmentation/`** — two HDUs: |
|
|
| ``` |
| HDU 0 160 × 128 float64 image, 4×4 block-averaged from the raw frame |
| HDU 1 160 × 128 int16 mask, values are exactly {0, 1}, header IMGTYPE = 'GROUND-TRUTH' |
| ``` |
|
|
| `1` is cloud and `0` is clear sky. Verified rather than assumed: across 600 sampled frames containing both classes, the mean ADU under `mask == 1` exceeds that under `mask == 0` in **100 %** of frames (DS1 1024 vs 901; DS2 1152 vs 1105). |
|
|
|  |
|
|
| Masks come from Otsu thresholding an `astropy` `LogStretch(a=1e5)` + `ZScaleInterval(contrast=0.9)` rendering of the frame (`utilities/mask_generator.py`), followed by visual inspection to discard frames with tree branches or buildings intruding at the corners. Treat them as an automatic baseline rather than human annotation. |
|
|
| ### Directory sharding on `raw/` |
|
|
| Both raw campaigns are split into per-minute subdirectories keyed on the timestamp already in each filename — 59 for `DATASET_1`, 24 for `DATASET_2` — because 18,748 files in one folder is more than the Hub browses comfortably: |
|
|
| ``` |
| raw/DATASET_1/2023_01_23_22_01/2023_01_23_22_01_54_358322_flux.fits |
| raw/DATASET_2/2023-03-30T21_27/2023-03-30T21_27_43.435747.fits |
| ``` |
|
|
| No training code reads the raw pool, so nothing depends on this. Flatten with `find raw/DATASET_1 -name '*.fits' -exec mv -t DATASET_1_flat/ {} +`. |
|
|
| ## Read before you train |
|
|
|  |
|
|
| **The masks are cloud-dominated, and every frame contains substantial cloud.** Median cloud fraction is 0.92 in DS1 and 0.96 in DS2; the mean is 0.85 and 0.90. About 11 % of DS1 frames and 12 % of DS2 frames exceed 99 % cloud, and the lowest frame in either subset sits near 45 %. A model predicting cloud everywhere already scores ~0.87 pixel accuracy, so report IoU on the clear-sky class, or train against a class-balanced loss. |
|
|
| **Consecutive frames sit a fraction of a second apart, so a random split leaks.** The raw campaigns run at an effective 5.0 Hz (`DATASET_1`) and 3.3 Hz (`DATASET_2`). After subsetting, consecutive selected frames are **0.66 s apart in DS1 and 0.32 s in DS2** (median; minimum 0.16 s and 0.21 s), and 8 % of DS1 gaps and 4 % of DS2 gaps exceed 2 s. Clouds move imperceptibly in a third of a second, so an i.i.d. shuffle puts visually identical frames on both sides of the split. Hold out a contiguous block of minutes instead; `manifests/` carries the timestamps needed to do it, and there is a snippet below. |
|
|
| **The classification labels are separable from texture alone.** `SIM_CLEAR` is synthetic, and its within-frame standard deviation clusters at 15–25 ADU while real cloudy frames span 20–280 ADU (right panel above). The synthetic frames also carry column-wise fixed-pattern noise and a central radial narcissus blob with a signature of their own. A classifier can therefore reach high accuracy by learning *synthetic versus real* in place of *clear versus cloudy*. The synthetic images exist because genuinely cloud-free frames were scarce across these two nights; read classification scores on this split as an upper bound, and validate on observed clear-sky frames. |
|
|
| **Both tasks validate on the same 20 % they early-stop on.** The reference notebooks split 80/20 at load time and report metrics on that validation subset, so a genuinely held-out test set has to be carved out before training. |
|
|
| ## Usage |
|
|
| The files are FITS, not a `datasets`-loadable format. Read them with `astropy` or `fitsio`. |
|
|
| Only what the models train on (~3 GB, skipping the raw campaigns): |
|
|
| ```bash |
| hf download ASKabalan/IRIS-CloudDeep --type dataset --local-dir data/ \ |
| --include 'segmentation/*' --include 'classification/*' --include 'manifests/*' |
| ``` |
|
|
| Everything, raw campaigns included: |
|
|
| ```bash |
| hf download ASKabalan/IRIS-CloudDeep --type dataset --local-dir data/ |
| ``` |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| |
| snapshot_download("ASKabalan/IRIS-CloudDeep", repo_type="dataset", local_dir="data", |
| allow_patterns=["segmentation/*", "classification/*", "manifests/*"]) |
| ``` |
|
|
| A segmentation sample, the way the reference UNet reads it: |
|
|
| ```python |
| from astropy.io import fits |
| |
| with fits.open("data/segmentation/DS1_MASKED_SUBSET/2023_01_23_22_01_54_569098_masked.fits") as hdul: |
| image = hdul[0].data / 2**14 # (128, 160) float64, in [0, 1] |
| mask = hdul[1].data # (128, 160), 1 = cloud |
| ``` |
|
|
| A classification sample. The raw 640×512 frame is block-averaged to 128×160 at load time; nothing is stored pre-binned: |
|
|
| ```python |
| import numpy as np |
| from astropy.io import fits |
| |
| def rebin(arr, new_shape=(128, 160)): |
| shape = (new_shape[0], arr.shape[0] // new_shape[0], |
| new_shape[1], arr.shape[1] // new_shape[1]) |
| return arr.reshape(shape).mean(-1).mean(1) |
| |
| image = rebin(fits.getdata("data/classification/CLOUD/2023_01_23_22_01_58_500596_flux.fits")) / 2**14 |
| ``` |
|
|
| A time-aware split, which is what the leakage note above argues for: |
|
|
| ```python |
| import glob, re |
| from datetime import datetime |
| |
| def stamp(path): # DS1 filenames: 2023_01_23_22_01_54_569098_masked.fits |
| m = re.search(r"(\d{4})_(\d\d)_(\d\d)_(\d\d)_(\d\d)_(\d\d)_(\d{6})", path) |
| return datetime(*map(int, m.groups())) |
| |
| files = sorted(glob.glob("data/segmentation/DS1_MASKED_SUBSET/*.fits"), key=stamp) |
| cut = int(0.8 * len(files)) |
| train, held_out = files[:cut], files[cut:] # contiguous in time, no shared minute |
| ``` |
|
|
| ## Provenance |
|
|
| `manifests/ds1_masked_subset.csv` and `ds2_masked_subset.csv` map each processed file to the raw frame it came from; `manifests/classification_labels.csv` gives the label of every classification file and, for the cloudy ones, its path under `raw/`. |
|
|
| The segmentation subsets are 2,951 of 18,748 frames and 1,494 of 4,746, sampled **without a recorded seed**, so the selection cannot be regenerated — those CSVs are the only record of it. |
|
|
| `classification/CLOUD` duplicates 1,413 frames already present under `raw/`, byte for byte (938 from `DATASET_1`, 475 from `DATASET_2`). It is kept as its own directory because the reference classifier globs it directly. |
|
|
| `classification/SIM_CLEAR` was produced by `utilities/synthetic_image.py`, which models a 2D horizontal sky gradient (downwelling radiance rising toward high zenith angle) plus read noise, fixed-pattern noise, bad pixel columns, sky noise and the narcissus effect. Parameters are recorded per file in the header, but the random draws were unseeded, so these exact realisations cannot be reproduced. |
|
|
| ## Public sky-image datasets are not included |
|
|
| Sommer et al. (2025) also evaluate the segmentation model on four third-party RGB sky-image databases. Their licences reserve redistribution to their own hosts — SWIMSEG is CC BY-NC 4.0, and HYTA is republished under permission granted to its host rather than transferably — so this repository holds only the LWIR data. Get the rest from the original sources: |
|
|
| - **HYTA** — Q. Li, W. Lu, J. Yang, *A Hybrid Thresholding Algorithm for Cloud Detection on Ground-Based Color Images*, J. Atmos. Oceanic Technol., 2011. The 3-level ground truth additionally requires S. Dev, Y. H. Lee, S. Winkler, *Multi-level semantic labelling of sky/cloud images*, IEEE ICIP, 2015. |
| - **SWIMSEG, SWINSEG, SWINYSEG** — Vision and Interaction Group, NUS / ADSC (Soumyabrata Dev, Stefan Winkler et al.). SWIMSEG is CC BY-NC 4.0. |
| - **WSISEG** — 400 whole-sky images labelled cloud / clear / undefined. |
|
|
| To rebuild the grayscale FITS the models consume, run `notebooks/convert_rgb_datasets_to_grayscale.ipynb` then `utilities/public_ds_process.py` from the [code repository](https://github.com/ASKabalan/infrared-cloud-detection). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{Sommer2025, |
| author = {Sommer, K. and Kabalan, W. and Brunet, R.}, |
| title = {Infrared radiometric image classification and segmentation of cloud |
| structures using a deep-learning framework from ground-based infrared |
| thermal camera observations}, |
| journal = {Atmospheric Measurement Techniques}, |
| volume = {18}, |
| number = {9}, |
| pages = {2083--2101}, |
| year = {2025}, |
| doi = {10.5194/amt-18-2083-2025} |
| } |
| ``` |
|
|
| The paper's data availability statement offers these datasets from the corresponding authors on request; this repository is that data, published so it can be cited and downloaded directly. |
|
|
| - Paper: <https://doi.org/10.5194/amt-18-2083-2025> |
| - Analysis code: <https://doi.org/10.5281/zenodo.15316607> — GitHub mirror at <https://github.com/ASKabalan/infrared-cloud-detection> |
| - FLIR Tau2 camera control: <https://doi.org/10.5281/zenodo.15311830> |
|
|
| ## Licence |
|
|
| CC BY 4.0, matching the paper. Please cite Sommer et al. (2025) if you use this data. |
|
|
