Source-linked AI summary
A Multi-Dimensional Score for Detecting Lesser-Known Points of Interest in Mountain Regions
Adriana Pompejano, Sébastien Laborie, Maxime Masson, Christian Sallaberry, Philippe Roose
TL;DR
Tourism concentrates visitors at iconic sites while nearby POIs remain less known, and existing approaches offer fragmented definitions and narrow ways of characterizing them. The paper proposes an open-data Obscurity Score that combines four dimensions to characterize mountain POIs. Preliminary Pyrenees results illustrate its use, while local-expert validation and weight calibration remain pending.
Problem
Lesser-known POI definitions are scattered, while prior approaches use narrow indicators or proprietary sources and questionnaires, limiting holistic characterization of mountain sites.
Source: §1 · pp. 1–2Method
The Obscurity Score combines International Awareness, Attractiveness, Promotion, and Accessibility using open data to characterize POI obscurity.
Source: §1 · p. 2, §3.1 · p. 3, §3.2 · p. 3Results
Preliminary Pyrenees case-study rankings under equal dimension weighting illustrate the score across cirques, peaks, and mountain huts, but still require local-expert validation and calibrated weights.
Source: §5 · p. 5Takeaways & Limitations
Identifying lesser-known mountain POIs is relevant to redistributing tourism demand and promoting territorial heritage.
Source: §1 · p. 1Sparse VGI documentation may reflect an inactive local mapping community rather than genuine obscurity, so active local mapping remains important for score trustworthiness.
Source: §5 · p. 5
Abstract
from arXiv · showhide
Overtourism concentrates visitor demand on few iconic sites, while other points of interest (POIs) in the same area often remain known only to a narrower public. This imbalance is especially critical in mountain environments, where limited infrastructure amplifies environmental pressures. Discovering lesser-known POIs is relevant for redistributing tourism demand spatially, valuing underrepresented heritage, and temporally, easing high seasonality pressures on popular POIs. Across the literature, definitions of lesser-known POIs or hidden gems remain scattered, and existing scoring approaches remain incomplete, relying on narrow indicators (e.g., number of photos, reviews, likes) which fail to characterize mountain POIs holistically. These approaches are further constrained by their reliance on commercial proprietary sources, or by questionnaires that are subject to memory bias. To address these gaps, we introduce a novel multi-dimensional metric, the Obscurity Score, that groups fragmented literature into four key dimensions (International Awareness, Attractiveness, Promotion, and Accessibility) to systematically detect and characterize lesser-known POIs and enable their promotion as a first step toward mitigating overtourism. To operationalize this score, we develop a generic pipeline mapping open data sources onto these four dimensions. Preliminary results from a web-based prototype demonstrate the practical relevance of the proposed approach.
1 Introduction
Tourism concentrates on iconic destinations while nearby places remain less visible, a particularly consequential imbalance in mountain environments. The paper proposes an open-data, four-dimensional Obscurity Score and a visual platform to characterize lesser-known mountain POIs.
1 Introduction
- Limited infrastructure and ecological fragility make concentrated visitation especially consequential in mountain environments, while identifying lesser-known POIs is relevant to dispersing demand and promoting territorial heritage.The introduction also notes that the imbalance includes both overcrowding at iconic attractions and the invisibility of other sites.Source: §1 · p. 1
- Popular recommendation platforms prioritize engagement and ratings, reinforcing attention to saturated sites while keeping alternatives less visible; generative-AI itinerary advice is described as unreliable and unsafe.The text attributes this pattern to collaborative-filtering recommendations based on high engagement and top user ratings.Source: §1 · p. 1
- Prior approaches often use isolated indicators, reduce recognition to popular-versus-hidden categories, and rely on proprietary databases or memory-biased questionnaires.The cited indicators include photo counts, distance, and review volume; the introduction notes that intermediate recognition tiers matter for assessing promotional margin.Source: §1 · pp. 1–2
- The Obscurity Score combines International Awareness, Attractiveness, Promotion, and Accessibility into a unified, continuous characterization of how well-known a mountain POI is.The dimensions capture recognition beyond the local context, user-generated photos, official marketing, and physical reachability.Source: §1 · p. 2
- The score maps POIs to four ordinal tiers—mainstream, known, partially known, and lesser-known—with higher values indicating greater obscurity.The introduction presents the score as a way to distinguish intermediate levels of site recognition as well as the endpoints.Source: §1 · p. 2
- The proposed open-data pipeline integrates, filters, and allows customization of the score, while a visual platform displays place types from mainstream to lesser-known within a territory.The platform is presented as decision support for stakeholders such as DMOs considering new tourism offerings or visitor-flow redistribution.Source: §1 · p. 2
2 Related Work
Related work contrasts proprietary POI sources with open volunteered geographic information, then reviews how lesser-known places are defined and characterized. It identifies a need to integrate diverse indicators in a score whose weights can adapt to local context.
2.1 POI Sources and Volunteered Geographic Information Quality
- Commercial POI datasets offer positional precision and verified business records but constrain storage, redistribution, and reproducibility, whereas OSM is openly licensed and continuously updated by contributors.VGI completeness and accuracy depend on local contributor engagement; mountain hiking and alpine communities have helped improve coverage in these regions.Source: §2.1 · p. 2
- Open sources can include lesser-known sites missing from commercial directories, and timestamped OSM edits allow newly mapped or modified POIs to be detected over time.Source: §2.1 · p. 2
2.2 Characterizing Lesser-Known POIs
- Lesser-known POIs commonly share low external visibility and scarce information, but definitions also emphasize weak promotion or local cultural significance; one study used fixed Google-results and platform-comment/photo thresholds.The example thresholds are fewer than 500,000 Google results and under 20 comments or photos on major platforms.Source: §2.2 · p. 2
- Prior indicators span international documentation, user-contributed photos as revealed attractiveness, dedicated promotional channels, and physical accessibility, including mountain topography and trail difficulty.The reviewed work links photos with visitation and distinguishes revealed interest from intrinsic aesthetic quality; promotional channels include DMO-managed websites and social media.Source: §2.2 · pp. 2–3
- Because existing studies isolate indicators or use rigid weighting methods such as PCA and entropy, the literature motivates a unified score with weights adaptable to local conditions.Source: §2.2 · p. 3
3 Methodology: a novel approach to detect lesser-known POIs
The methodology groups POI recognition into four complementary dimensions, combines them in a configurable Obscurity Score, and outlines an open-data pipeline for classifying mountain POIs.
3 Methodology: a novel approach to detect lesser-known POIs
- The methodology presents the dimensions, score, and pipeline as a generic approach that can accept different data sources, adapt as data evolve, and support multiple languages.Source: §3 · p. 3, §3.3 · p. 4
3.1 A multi-dimensional analysis of POIs
- Four complementary dimensions—International Awareness, Attractiveness, Promotion, and Accessibility—jointly capture recognition aspects that existing reviewed frameworks do not integrate.They distinguish global from regional recognition, organic popularity from active marketing, remoteness from obscurity, and revealed appeal from intrinsic appeal.Source: §3.1 · p. 3
3.2 The Obscurity Score
- The configurable Obscurity Score combines normalized dimension values using weights summing to one, with higher dimension values indicating stronger obscurity signals.Weights begin at 0.25 each and are planned for grid-search calibration against specialist judgments; the score is classified into four tiers using Jenks natural breaks when weights are uncalibrated.Source: §3.2 · pp. 3–4
- For two huts about 2.8 km apart, Refuge des Espuguettes scores 0.55 and is classified mainstream, while Cabane de Labassa scores 0.90 and is lesser-known.The example illustrates how the score distinguishes sites near the same landmark.Source: Figure 1 · p. 4, §3.2 · p. 4
3.3 Pipeline Overview
- The pipeline collects and pre-filters VGI POIs, enriches them with open sources, scores and ranks them, then calibrates weights against expert judgments and evaluates on excluded experts.Unnamed POIs without photos are excluded, and near-identical features are deduplicated.Source: §3.3 · p. 4
4 Prototype and Preliminary Use Case
The prototype applies the Obscurity Score to a Pyrenees case study, illustrating POI classifications from open data while leaving calibration and expert validation for future work.
Prototype and study setup
- The web prototype maps results for a selected area; the study area spans the French-Spanish Pyrenees and uses OSM POIs with indicators from Wikidata, Wikimedia, and OSRM.The area was selected for its range of POI types and high density of eligible POIs, rather than for popularity.Source: §4 · p. 4
Preliminary results
- Of 183 scored POIs, 41 were lesser-known, 67 partially known, 61 known and 14 mainstream.The case study retrieved 499 raw POIs, of which 316 were excluded after pre-filtering and spatial deduplication.Source: §4 · p. 4
Illustrative scoring
- For International Awareness, Labassa’s absent Wikipedia article gives D1(p2) = 1.00, while Espuguettes’ article and 68 monthly pageviews yield D1(p1) = 0.79.Espuguettes’ pageviews fall in the ≤200 bin; its language and pageview scores are averaged.Source: §4 · pp. 4–5
- For Attractiveness, Espuguettes scores D2(p1) = 0.51 and Labassa scores D2(p2) = 0.91 from Commons photos and OSM edits.The dimension combines photo score and mapping maturity using a 0.65/0.35 split.Source: §4 · p. 5
- Both huts sit on a national route, but only Espuguettes has an online presence; their promotion scores are 0.60 and 0.75, while access scores are 0.32 and 0.94, respectively.Accessibility maps routed walking time through a smooth curve centered at 45 minutes; Espuguettes is a 24-minute walk and Labassa a 2h29 walk.Source: §4 · p. 5
Validation and design limits
- The 𝐷2 bin boundaries and sub-weights and the 45-minute 𝐷4 midpoint are design defaults, and expert validation and weight calibration remain ongoing.The authors say refining defaults against a larger expert-validated sample remains future work.Source: §4 · p. 5
5 Discussion and preliminary results
The preliminary case study ranks same-category POIs under equal dimension weighting and pairs mainstream sites with less-known alternatives; the authors identify data, temporal, and validation caveats.
Preliminary results
- Under equal 0.25 dimension weights, the Pyrenees case study ranks cirques, peaks and mountain huts, pairing each mainstream POI with a nearby known, partially known or lesser-known alternative.The paired examples include Refuge des Espuguettes and Cabane de Labassa; local experts still need to validate the preliminary rankings and calibrate weights.Source: §5 · p. 5, Table 2 · p. 6
Limitations
- Sparse VGI may reflect an inactive mapper community rather than genuine obscurity, so trustworthy scoring requires an initial completeness filter and an active local mapping community.The paper treats low visibility or documentation as evidence of being lesser-known, following prior work.Source: §5 · p. 5
- Recognition changes over time, and publicizing lesser-known sites may redirect visitors and risk new overtourism, motivating a future carrying-capacity layer for each POI.The authors propose treating the score as fluid rather than as a fixed label.Source: §5 · p. 5
Further comparison
- The authors propose comparing the Obscurity Score with LLM judgments against local expert assessments, while noting that their open-data pipeline remains free and reproducible.They identify hybrid designs combining LLMs and the pipeline as a possible direction even if LLMs match or exceed the score.Source: §5 · p. 5
6 Conclusion and Future Work
The paper concludes that an open-data pipeline and scoring system can identify lesser-known mountain POIs, with preliminary Pyrenees results; broader calibration, recommendation, and capacity-aware work remain ahead.
Conclusion
- The authors demonstrate the potential of a generic open-data pipeline and dedicated scoring system through preliminary same-category POI comparisons in the Pyrenees.Expert-in-the-loop calibration remains future work while validation data collection is ongoing.Source: §6 · p. 6
Future work
- Future calibration across territories and POI types will test whether score weights remain stable across contexts.The paper notes that the expert-in-the-loop calibration itself has not yet been completed.Source: §6 · p. 6
- The authors propose extending same-category comparisons into a recommendation system and adding carrying-capacity information so suggested lesser-known sites can be matched to their ability to host visitors.The proposed recommendations would also consider trail distance, elevation gain, difficulty, site quality, and similarity.Source: §6 · p. 6