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End of preview. Expand
in Data Studio
ThermBench 🔥 — Thermal Heatmap Source Localization Benchmark
📝 Summary
ThermBench is a physics-inspired synthetic dataset designed to evaluate algorithms that infer hidden thermal sources from an observed heat diffusion map.
Each data sample contains:
- an observed heatmap (matrix of values),
- and the ground-truth sources:
(row, col, intensity).
Diffusion follows inverse Manhattan distance:
[ H(i,j) ;=; \sum_{s=1}^{K} \frac{I_s}{d(i,j,s)+1} ]
where (d) is the Manhattan distance to source (s).
📊 Dataset Structure
- level: Difficulty tier (
very_easy,easy,medium,hard,extreme) - input_text: Heatmap formatted as:
N M K <N rows of values> - output_text: True source positions and intensities in format:
row col intensity
Example
{
"level": "easy",
"input_text": "5 5\n2\n10 8 6 5 4\n8 10 7 6 5\n6 7 10 7 6\n5 6 7 10 8\n4 5 6 8 10",
"output_text": "1 1 10.0\n5 5 10.0"
}
🚀 Usage
from datasets import load_dataset
dataset = load_dataset(
"ZoneTwelve/Thermal-Heatmap-Source-Localization",
split="train"
)
print(dataset[0])
🎚 Difficulty Levels
- very_easy → 3×3 grid, 1 source
- easy → 5×5 grid, 2 sources
- medium → 10×10 grid, 3 sources
- hard → 20×20 grid, 5 sources
- extreme → 30×30 grid, 7 sources
Each level contains 100 samples → 500 total.
A fuzzy extension of ThermBench introduces noise, intensity jitter, and rounding differences to simulate real‑world sensor readings.
🔧 Intended Applications
- Benchmarking inverse problem solvers
- Robustness studies for optimization/AI
- Educational resource for algorithm development
📜 License
Apache License 2.0 © ZoneTwelve
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