Update dataset with new benchmark results and README

Co-authored-by: geogpt69 <[email protected]>
Co-authored-by: viserjor <[email protected]>

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README.md

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+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: emulation
+tags:
+- emulation
+- atmosphere radiative transfer models
+- hyperspectral
+pretty_name: Atmospheric Radiative Transfer Emulation Challenge
+---
+Last update: 08-05-2025
+
+<img src="https://elias-ai.eu/wp-content/uploads/2023/09/elias_logo_big-1.png" alt="elias_logo" style="width:15%; display: inline-block; margin-right: 150px;">
+<img src="https://elias-ai.eu/wp-content/uploads/2024/01/EN_FundedbytheEU_RGB_WHITE-Outline-1.png" alt="eu_logo" style="width:20%; display: inline-block;">
+
+# **Atmospheric Radiative Transfer Emulation Challenge**
+
+
+1. [**Introduction**](#introduction)
+2. [**Challenge Tasks and Data**](#challenge-tasks-and-data):
+   
+    2.1. [**Proposed Experiments**](#proposed-experiments) 
+
+    2.2. [**Data Availability and Format**](#data-availability-and-format)
+3. [**Evaluation methodology**](#evaluation-methodology)
+  
+    3.1. [**Prediction Accuracy**](#prediction-accuracy)
+    
+    3.2. [**Computational efficiency**](#computational-efficiency)
+    
+    3.3. [**Proposed Protocol**](#proposed-protocol)
+
+4. [**Expected Outcomes**](#expected-outcomes)
+
+
+
+## **Benchmark Results**
+
+| **Model** | **MRE A1 (%)** | **MRE A2 (%)** | **MRE B1 (%)** | **MRE B2 (%)** | **Score** | **Runtime** | **Rank** |
+|-----------|---------------|---------------|---------------|---------------|----------|----------|--------|
+| Krtek | 0.545 | 7.693 | 0.816 | 7.877 | 1.175 | 0.526 | 1° |
+| baseline | 0.998 | 12.604 | 1.065 | 7.072 | 1.825 | 2.400 | 2° |
+
+## **Introduction**
+
+Atmospheric Radiative Transfer Models (RTM) are crucial in Earth and climate sciences with applications such as synthetic scene generation, satellite data processing, or
+numerical weather forecasting. However, their increasing complexity results in a computational burden that limits direct use in operational settings. A practical solution
+is to interpolate look-up-tables (LUTs) of pre-computed RTM simulations generated from long and costly model runs. However, large LUTs are still needed to achieve accurate
+results, requiring significant time to generate and demanding high memory capacity. Alternative, ad hoc solutions make data processing algorithms mission-specific and
+lack generalization. These problems are exacerbated for hyperspectral satellite missions, where the data volume of LUTs can increase by one or two orders of magnitude,
+limiting the applicability of advanced data processing algorithms. In this context, emulation offers an alternative, allowing for real-time satellite data processing
+algorithms while providing high prediction accuracy and adaptability across atmospheric conditions. Emulation replicate the behavior of a deterministic and computationally
+demanding model using statistical regression algorithms. This approach facilitates the implementation of physics-based inversion algorithms, yielding accurate and
+computationally efficient model predictions compared to traditional look-up table interpolation methods.
+
+RTM emulation is challenging due to the high-dimensional nature of both input (~10 dimensions) and output (several thousand) spaces, and the complex interactions of
+electromagnetic radiation with the  atmosphere. The research implications are vast, with potential breakthroughs in surrogate modeling, uncertainty quantification,
+and physics-aware AI systems that can significantly contribute to climate and Earth observation sciences.
+
+This challenge will contribute to reducing computational burdens in climate and atmospheric research, enabling (1) Faster satellite data processing for applications in
+remote sensing and weather prediction, (2) improved accuracy in atmospheric correction of hyperspectral imaging data, and (3) more efficient climate simulations, allowing
+broader exploration of emission pathways aligned with sustainability goals. 
+
+## **Challenge Tasks and Data**
+
+Participants in this challenge will develop emulators trained on provided datasets to predict spectral magnitudes (atmospheric transmittances and reflectances)
+based on input atmospheric and geometric conditions. The challenge is structured around three main tasks: (1) training ML models
+using predefined datasets, (2) predicting outputs for given test conditions, and (3) evaluating emulator performance based on accuracy.
+
+### **Proposed Experiments**
+
+The challenge includes two primary application test scenarios:
+1. **Atmospheric Correction** (`A`): This scenario focuses on the atmospheric correction of hyperspectral satellite imaging data. Emulators will be tested on
+   their ability to reproduce key atmospheric transfer functions that influence radiance measurements. This includes path radiance, direct/diffuse solar irradiance, and
+   transmittance properties. Full spectral range simulations (400-2500 nm) will be provided at a resolution of 5cm<sup>-1</sup>.
+2. **CO<sub>2</sub> Column Retrieval** (`B`): This scenario is in the context of atmospheric CO<sub>2</sub> retrieval by modeling how radiation interacts with various gas
+   layers. The emulators will be evaluated on their accuracy in predicting top-of-atmosphere radiance, particularly within the spectral range sensitive to CO<sub>2</sub>
+   absorption (2000-2100 nm) at high spectral resolution (0.1cm<sup>-1</sup>).
+
+For both scenarios, two test datasets (tracks) will be provided to evaluate 1) interpolation, and 2) extrapolation.
+
+Each scenario-track combination will be identified using  alphanumeric ID `Sn`, where `S`={`A`,`B`} denotes to the scenario, and `n`={1,2} 
+represents test dataset type (i.e., track). For example, `A2` refers to prediction for the atmospheric correction scenario using the the extrapolation dataset. 
+
+Participants may choose their preferred scenario(s) and tracks; however, we encourage submitting predictions for all test conditions.
+
+### **Data Availability and Format**
+
+Participants will have access to multiple training datasets of atmospheric RTM simulations varying in sample sizes, input parameters, and spectral range/resolution. 
+These datasets will be generated using Latin Hypercube Sampling to ensure a comprehensive input space coverage and minimize issues related to ill-posedness and
+unrealistic results.
+
+The training data (i.e., inputs and outputs of RTM simulations) will be stored in [HDF5](https://docs.h5py.org/en/stable/) format with the following structure:
+
+| **Dimensions** | |
+|:---:|:---:|
+| **Name** | **Description** |
+| `n_wl` | Number of wavelengths for which spectral data is provided |
+| `n_funcs` | Number of atmospheric transfer functions |
+| `n_comb` | Number of data points at which spectral data is provided |
+| `n_param` | Dimensionality of the input variable space |
+
+| **Data Components** | | | |
+|:---:|:---:|:---:|:---:|
+| **Name** | **Description** | **Dimensions** | **Datatype** |
+| **`LUTdata`** | Atmospheric transfer functions (i.e. outputs) | `n_funcs*n_wvl x n_comb` | single |
+| **`LUTHeader`** | Matrix of input variable values for each combination (i.e., inputs) | `n_param x n_comb` | double |
+| **`wvl`** | Wavelength values associated with the atmospheric transfer functions (i.e., spectral grid) | `n_wvl` | double |
+
+**Note:** Participants may choose to predict the spectral data either as a single vector of length `n_funcs*n_wvl` or as `n_funcs` separate vectors of lenght `n_wvl`.
+
+Testing input datasets (i.e., input for predictions) will be stored in a tabulated `.csv` format with dimensions `n_param x n_comb`.
+
+The trainng and testing dataset will be organized organized into scenario-specific folders (see 
+[**Proposed experiments**](/datasets/isp-uv-es/rtm_emulation#proposed-experiments)): `scenarioA` (Atmospheric Correction), and `scenarioB` (CO<sub>2</sub> Column Retrieval).
+Each folder will contain:
+- A `train` with multiple `.h5` files corresponding to different training sample sizes (e.g. `train2000.h5`contains 2000 samples).
+- A `reference` subfolder containg two test files (`refInterp` and `refExtrap`) referring to the two aforementioned tracks (i.e., interpolation and extrapolation).
+
+Here is an example of how  to load each dataset in python:
+```{python}
+import h5py
+import pandas as pd
+import numpy as np
+
+# Replace with the actual path to your training and testing data
+trainFile = 'train2000.h5'
+testFile = 'refInterp.csv'
+
+# Open the H5 file
+with h5py.File(file_path, 'r') as h5_file
+Ytrain = h5_file['LUTdata'][:]
+Xtrain = h5_file['LUTHeader'][:]
+wvl = h5_file['wvl'][:]
+
+# Read testing data
+df = pd.read_csv(testFile)
+Xtest = df.to_numpy()
+```
+
+in Matlab:
+```{matlab}
+# Replace with the actual path to your training and testing data
+trainFile = 'train2000.h5';
+testFile = 'refInterp.csv';
+
+# Open the H5 file
+Ytrain = h5read(trainFile,'/LUTdata');
+Xtrain = h5read(trainFile,'/LUTheader');
+wvl = h5read(trainFile,'/wvl');
+
+# Read testing data
+Xtest = importdata(testFile);
+```
+
+and in R language:
+```{r}
+library(rhdf5)
+
+# Replace with the actual path to your training and testing data
+trainFile <- "train2000.h5"
+testFile <- "refInterp.csv"
+
+# Open the H5 file
+lut_data <- h5read(file_path, "LUTdata")
+lut_header <- h5read(file_path, "LUTHeader")
+wavelengths <- h5read(file_path, "wvl")
+
+# Read testing data
+Xtest <- as.matrix(read.table(file_path, sep = ",", header = TRUE))
+```
+
+All data will be shared through a this [repository](ttps://huggingface.co/datasets/isp-uv-es/rtm_emulation/tree/main). After the challenge finishes, participants
+will also have access to the evaluation scripts on [this GitLab](http://to_be_prepared) to ensure transparency and reproducibility.
+
+
+## **Evaluation methodology**
+
+The evaluation will focus on three key aspects: prediction accuracy, computational efficiency, and extrapolation performance.
+
+### **Prediction Accuracy**
+
+For the **atmospheric correction** scenario (`A`), the predicted atmospheric transfer functions will be used to retrieve surface reflectance from the top-of-atmosphere
+(TOA) radiance simulations in the testing dataset. The evaluation will proceed as follows:
+1. The relative difference between retrieved and reference reflectance will be computed for each spectral channel and sample from the testing dataset.
+2. The mean relative error (MRE) will be calculated over the enrire reference dataset to assess overall emulator bias.
+3. The spectrally-averaged MRE (MRE<sub>λ</sub> will be computed, excluding wavelengths in the deep H<sub>2</sub>O. absorption regions, to ensure direct comparability between participants.
+
+For the **CO<sub>2</sub> retrieval** scenario (`B`), evaluation will follow the same steps, comparing predicted TOA radiance spectral data against the reference values
+in the testing dataset. 
+
+Since each participant/model can contribute to up to four scenario-track combinations, we will consolidate results into a single final ranking using the following process:
+1. **Individual ranking**: For each of the four combinations, submissions will be ranked based on their MRE<sub>λ</sub> values. Lower MRE<sub>λ</sub> values correspond to
+    better performance. In the unlikely case of ties, these will be handled by averaging the tied ranks.
+2. **Final ranking**: Rankings will be aggregated into a single final score using a weighted average. The following weights will be applied: 0.375 for interpolation and
+   0.175 for extrapolation tracks. That is:
+   **Final score = (0.325 × AC-Interp Rank) + (0.175 × AC-Extrap Rank) + (0.325 × CO2-Interp Rank) + (0.175 × CO2-Extrap Rank)**
+3. **Missing Submissions**: If a participant does not submit results for a particular scenario-track combination, they will be placed in the last position for that track.
+
+To ensure fairness in the final ranking, we will use the **standard competition ranking** method in the case of ties. If two or more participants achieve the same
+weighted average rank, they will be assigned the same final position, and the subsequent rank(s) will be skipped accordingly. For example, if two participants are tied
+for 1st place, they will both receive rank 1, and the next participant will be ranked 3rd (not 2nd).
+
+**Note:** while the challenge is open, the daily evaluation of error metrics will be done on a subset of the test data. This will avoid participants to have detailed 
+information that would allow them to fine-tune their models. The final results and ranking evaluated with all the validation data will be provided and the end-date of the challenge.  
+
+### **Computational efficiency**
+Participants must report the runtime required to generate predictions across different emulator configurations. The average runtime of all scenario-track combinations
+will be calculated and reported in the table. **Runtime won't be taken into account for the final ranking**. After the competition ends, and to facilitate fair
+comparisons, participants will be requested to provide a report with hardware specifications, including: CPU, Parallelization settings (e.g., multi-threading, GPU
+acceleration), RAM availability. Additionally, participants should report key model characteristics, such as the number of operations required for a single prediction and the number of trainable
+parameters in their ML models.
+
+All evaluation scripts will be publicly available on GitLab and Huggingface to ensure fairness, trustworthiness, and transparency.
+
+### **Proposed Protocol**
+
+- Participant must generate emulator predictions on the provided testing datasets before the submission deadline. Multiple emulator models can be submitted.
+
+- The submission will be made via a [pull request](https://huggingface.co/docs/hub/en/repositories-pull-requests-discussions) to this repository.
+
+- Each submission **MUST** include the prediction results in hdf5 format and a `metadata.json`. 
+
+- The predictions should be stored in a `.h5`file with the same format as the [training data](/datasets/isp-uv-es/rtm_emulation#data-availability-and-format). 
+  Note that only the **`LUTdata`** matrix (i.e., the predictions) are needed. A baseline example of this file is available for participants (`baseline_Sn.h5`).
+  We encourage participants to compress their hdf5 files using the deflate option.
+
+- Each prediction file must be stored in`predictions` subfolder within the corresponding
+  scenario folder (e.g., (e.g. `/scenarioA/predictions`). The prediction files should be named using the emulator/model name followed by the scenario-track ID
+ (e.g. `/scenarioA/predictions/mymodel_A1.h5`). A global attributed named `runtime`must be included to report the
+  computational efficiency of your model (value expressed in seconds).
+  Note that all predictions for different scenario-tracks should be stored in separate files.
+
+- The metadata file (`metadata.json`) shall contain the following information:
+
+```{json}
+{
+  "name": "model_name",
+  "authors": ["author1", "author2"],
+  "affiliations": ["affiliation1", "affiliation2"],
+  "description": "A brief description of the emulator",
+  "url": "[OPTIONAL] URL to the model repository if it is open-source",
+  "doi": "DOI to the model publication (if available)",
+  "email": <main_contact_email>
+}
+```
+
+- Emulator predictions will be evaluated once per day at 12:00 CET based on the defined metrics.
+
+- After the deadline, teams will be contacted with their evaluation results. If any issues are identified, theams will have up to two 
+weeks to provide the necessary corrections.
+
+- In case of **problems with the pull request** or incorrect validity of the submitted files, all discussions will be held in the [discussion board](https://huggingface.co/isp-uv-es/rtm_emulation/discussions).
+
+- After all the participants have provided the necessary corrections, the results will be published in the discussion section of this repository.
+
+
+## **Expected Outcomes**
+
+- No clear superiority of any methodology in all metrics is expected. 
+- Participants will benefit from the analysis on scenarios/tracks, which will serve them to improve their models.
+- A research publication will be submitted to a remote sensing journal with the top three winners.
+- An overview paper of the challenge will be published at the [ECML-PKDD 2025](https://ecmlpkdd.org/2025/) workshop proceedings.
+- The winner will get covered the registratin cost for the [ECML-PKDD 2025](https://ecmlpkdd.org/2025/).
+- We are exploring the possibility to provid an economic prizes for the top three winners. Stay tuned!

results/baseline.json

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+{
+  "name": "baseline",
+  "authors": ["Jorge Vicent Servera"],
+  "affiliations": ["Image & Signal Processing (ISP)"],
+  "description": "2nd order hypersurface polynomial fitting",
+  "url": "",
+  "doi": "",
+  "email": "[email protected]"
+}

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