TEMPLAR Wales: A georeferenced environmental and toponymic dataset of Welsh settlements
- URL: http://arxiv.org/abs/2608.26970v1
- Date: Thu, 27 Aug 2026 11:14:57 GMT
- Title: TEMPLAR Wales: A georeferenced environmental and toponymic dataset of Welsh settlements
- Abstract summary: TEMPLAR Wales is a georeferenced environmental-toponymy dataset comprising 3,757 settlement records across Wales.<n>The resource links a reproducible settlement frame to deterministic lexical screening and settlement-level environmental attributes.<n>It contains 1,350 lexical detections across 1,294 settlements, generated from a frozen registry of 24 Welsh place-name elements.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Place names provide persistent records of how landscapes have been described and organised, but their quantitative reuse requires explicit separation between mapped places, lexical annotations and environmental measurements. TEMPLAR Wales is a georeferenced environmental-toponymy dataset comprising 3,757 settlement records across Wales. The resource links a reproducible settlement frame to deterministic lexical screening and settlement-level environmental attributes through stable identifiers. It contains 1,350 lexical detections across 1,294 settlements, generated from a frozen registry of 24 Welsh place-name elements, while retaining exact- and prefix-token matches and their provenance separately. Environmental attributes describe river and coastal proximity, elevation and local terrain context at multiple spatial scales, land cover and neighbourhood woody cover, with parallel terrain measurements derived from independent elevation products. The dataset is distributed as four relational tables accompanied by a field-level data dictionary, source-provenance register and licensing metadata. Technical validation confirms relational integrity, deterministic lexical reconstruction, documented environmental coverage, strong agreement between independent terrain sources and reproducible reconstruction of the frozen release. TEMPLAR Wales provides a reusable foundation for research in toponymy, linguistic geography, historical and environmental landscape studies, GIS and spatial data analysis without treating computational lexical detections as verified etymologies or contemporary environmental measurements as historical landscape reconstructions.
Related papers
- Terrain signatures in Welsh settlement names [0.0]
We analysed 3,757 Welsh settlements using a frozen, source-audited 24-element lexical framework.<n>High-terrain names occupied locations 24.4 m higher relative to their 2-km surroundings.<n>Adding terrain-name polarity to a non-lexical spatial and settlement baseline reduced geographically held-out mean squared error by 4.63%.
arXiv Detail & Related papers (2026-08-27T11:24:11Z) - Georeferencing Non-Gazetteered Place Names using Biological Specimen Records [1.9911463513783276]
We use digitised data from the Allan Herbarium (New Zealand) to identify non-gazetteer place names (NGPs)<n>These place names are historical, vernacular, or colloquial and were used as landmarks to describe a specimen's location at the time of collection.<n>We leverage repeated occurrences of the same place name across specimen records with different specimen locations and spatial relation terms to derive constraints on NGP locations.
arXiv Detail & Related papers (2026-08-07T07:15:38Z) - Distributional Open-Ended Evaluation of LLM Cultural Value Alignment Based on Value Codebook [50.10594064510559]
DOVE is a distributional evaluation framework that directly compares human-written text with LLM-generated outputs.<n>We show that DOVE achieves superior predictive validity, attaining a 31.56% correlation with downstream tasks, while maintaining high reliability with as few as 500 samples per culture.
arXiv Detail & Related papers (2026-03-16T08:33:10Z) - The Chronicles of RiDiC: Generating Datasets with Controlled Popularity Distribution for Long-form Factuality Evaluation [56.22548620937169]
The RiDiC dataset contains 3,000 entities from three domains -- rivers, natural disasters, and car models -- spanning different popularity tiers.<n>Each entity is accompanied by its geographical location, English and Chinese names, and relevant English and Chinese Wikipedia content.<n>These were then evaluated using a third-party factuality checker, which showed that entities from our dataset caused even frontier models to hallucinate.
arXiv Detail & Related papers (2026-03-11T01:02:55Z) - Physically Interpretable AlphaEarth Foundation Model Embeddings Enable LLM-Based Land Surface Intelligence [0.0]
We present a comprehensive interpretability analysis of Google AlphaEarth's 64-dimensional embeddings against 26 environmental variables.<n>We then developed a Land Surface Intelligence system that implements retrieval-augmented generation over a FAISS-indexed embedding database of 12.1 million vectors.
arXiv Detail & Related papers (2026-02-10T22:58:50Z) - Spatial Context Improves the Integration of Text with Remote Sensing for Mapping Environmental Variables [19.670023742796136]
We propose an attention-based approach that combines aerial imagery and geolocated text within a spatial neighbourhood.<n>Our model is evaluated on the task of predicting 103 environmental variables from the SWECO25 data cube.
arXiv Detail & Related papers (2026-01-13T17:27:16Z) - Georeferencing complex relative locality descriptions with large language models [1.9911463513783276]
This paper explores the potential of Large Language Models to georeference complex locality descriptions automatically.<n>We first identified effective prompting patterns, then fine-tuned an LLM using Quantized Low-Rank Adaptation (QLoRA) on biodiversity datasets.<n>Our approach outperforms existing baselines with an average, across datasets, of 65% of records within a 10 km radius, for a fixed amount of training data.
arXiv Detail & Related papers (2025-12-16T09:27:02Z) - Combining Observational Data and Language for Species Range Estimation [63.65684199946094]
We propose a novel approach combining millions of citizen science species observations with textual descriptions from Wikipedia.<n>Our framework maps locations, species, and text descriptions into a common space, enabling zero-shot range estimation from textual descriptions.<n>Our approach also acts as a strong prior when combined with observational data, resulting in more accurate range estimation with less data.
arXiv Detail & Related papers (2024-10-14T17:22:55Z) - GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark [56.08664336835741]
We propose a GeoGraphic Language Understanding Evaluation benchmark, named GeoGLUE.
We collect data from open-released geographic resources and introduce six natural language understanding tasks.
We pro vide evaluation experiments and analysis of general baselines, indicating the effectiveness and significance of the GeoGLUE benchmark.
arXiv Detail & Related papers (2023-05-11T03:21:56Z) - Robust Self-Tuning Data Association for Geo-Referencing Using Lane Markings [44.4879068879732]
This paper presents a complete pipeline for resolving ambiguities during the data association.
Its core is a robust self-tuning data association that adapts the search area depending on the entropy of the measurements.
We evaluate our method on real data from urban and rural scenarios around the city of Karlsruhe in Germany.
arXiv Detail & Related papers (2022-07-28T12:29:39Z) - Jalisco's multiclass land cover analysis and classification using a
novel lightweight convnet with real-world multispectral and relief data [51.715517570634994]
We present our novel lightweight (only 89k parameters) Convolution Neural Network (ConvNet) to make LC classification and analysis.
In this work, we combine three real-world open data sources to obtain 13 channels.
Our embedded analysis anticipates the limited performance in some classes and gives us the opportunity to group the most similar.
arXiv Detail & Related papers (2022-01-26T14:58:51Z) - SIRI: Spatial Relation Induced Network For Spatial Description
Resolution [64.38872296406211]
We propose a novel relationship induced (SIRI) network for language-guided localization.
We show that our method is around 24% better than the state-of-the-art method in terms of accuracy, measured by an 80-pixel radius.
Our method also generalizes well on our proposed extended dataset collected using the same settings as Touchdown.
arXiv Detail & Related papers (2020-10-27T14:04:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.