Datasets:

ZoneTwelve
/

Thermal-Heatmap-Source-Localization

datasets:
  - ZoneTwelve/Thermal-Heatmap-Source-Localization
tags:
  - benchmark
  - heatmap
  - physics
  - source-localization
  - synthetic
license: apache-2.0
pretty_name: Thermal Heatmap Source Localization (ThermBench)

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