Someone is lost in the wilderness. A drone can cover more ground in an hour than a ground team can in a day — but where should it look? That question is exactly what SAREnv helps answer, and it's why benchmarks matter: you can't improve search strategies if everyone measures against a different reality.
Published open-access in the journal Drones, SAREnv gives the UAV search-and-rescue community a shared dataset and evaluation framework for informed wilderness SAR planning. Figure 1 shows the environment model: four nested Regions of Interest centered on the Initial Planning Point, a probability map based on the lost person model, and potential search paths that exploit it.
Where do lost people actually end up?
The environmental model is grounded in empirical data from Lost Person Behavior (Koester, 2008), which establishes strong correlations between where people are found and specific terrain features. Table 1 reports the find-location distribution by feature class — linear features like trails and streams dominate, which is why SAREnv buffers them into searchable two-dimensional areas.
| Find Location (%) | Temperate | Dry |
|---|---|---|
| n | 312 | 196 |
| Structure | 13% | 10% |
| Road | 13% | 17% |
| Linear | 25% | 31% |
| Drainage | 12% | 18% |
| Water | 8% | 9% |
| Brush | 2% | 2% |
| Scrub | 3% | 3% |
| Woods | 7% | 6% |
| Field | 14% | 1% |
| Rock | 4% | 2% |
Based on empirical data from Lost Person Behavior (Koester, 2008): linear features dominate (25–31%).
Nested regions of interest
RoIs are circular areas centered on the Initial Planning Point, with sizes statistically derived from dispersal patterns. Table 2 defines the four nested RoIs (small, medium, large, extra large) for each environment type — the extra-large RoI can reach a 19.3 km radius (1170 km²). Figure 2 visualizes the polygonal features extracted within the extra-large RoI of a temperate-mountain environment (dataset #19).
| Horizontal distance from the IPP* | Temp. Mountains | Temp. Flat | Dry Mountains | Dry Flat |
|---|---|---|---|---|
| n | 568 | 274 | 221 | 58 |
| Small | 1.1 (3.8) | 0.6 (1.1) | 1.6 (8.0) | 1.3 (5.3) |
| Medium | 3.1 (30.2) | 1.8 (10.2) | 3.2 (32.2) | 2.1 (13.8) |
| Large | 5.8 (105.7) | 3.2 (32.2) | 6.5 (132.7) | 6.6 (136.8) |
| Extra Large | 18.3 (1052.1) | 9.9 (307.9) | 19.3 (1170.2) | 13.1 (539.1) |
Shown in the format: radii (km) (area (km²)). Extra-large RoI up to 19.3 km radius (1170 km²).
Generating realistic scenarios
SAREnv generates lost-person locations that cluster in high-probability areas. Figure 3 shows the probability map of environment #19, and Figure 4 shows 300 generated locations distributed across the search environment.
Comparing search strategies
Four baseline path planners ship with the benchmark — Concentric Circles, Pizza Zigzag, Greedy, and Random Exploration — shown in Figure 5 over a medium probability heatmap. Search strategies can then be compared head-to-head on the same scenarios with three performance metrics.
Figure 6 evaluates the baselines across the complete dataset of 60 environments: exhaustive methods generally outperform exploratory ones on accumulated probability of detection, but when the budget is tight — or the metric is time-discounted, favoring initial exploration — greedy and random exploration hold their own.
The project is open source and lives under the NAMUR GitHub organization, growing out of the WildDrone/HERD collaboration with the University of Bristol. It's already being put to work: University of Bristol undergraduates extended and deployed SAREnv scenarios for their third-year projects, presented at the EuroDroS conference at SDU.
2025 · Drones (MDPI)MDPIGitHubVideo
K.A.R. Grøntved, A. Jarabo-Peñas, S. Reid, E.G.A. Rolland, M. Watson, A. Richards, S. Bullock, A.L. Christensen