Opportunities and challenges provided by regional-scale LiDAR data sets
Notice bibliographique
Résumé
Light detection and Ranging (LiDAR) systems are now widely regarded as the preferred source data for a range of terrain mapping applications owing to their high spatial accuracy, dense surface sampling, and the ability of laser scanners to map topography beneath forest and other vegetation covers. In the past, the prohibitive expense and difficulty involved in LiDAR acquisition meant that these data were most often collected in response to project-specific needs and for relatively small spatial extents. In most jurisdictions, the patchwork of LiDAR data that were publicly available were unlikely to allow for regional-scale analyses and applications. However, the decreased cost of acquisition that has occurred over the past decade, and the proliferation of LiDAR data providers, has changed this situation significantly. An increasing number of municipal, regional, provincial and federal governments have been involved in large-scale LiDAR data acquisition campaigns, the result of which has been the availability of regional-scale fine-resolution digital elevation models (DEMs) for use by researchers, practitioners, and other stakeholders. For example, a recent LiDAR acquisition project carried out in Ontario will soon make aerial LiDAR data publicly available in large portions of the province. The recent availability of extensive LiDAR data sets has been marked by a period of exploration, as practitioners work to replace older topographic data with LiDAR and as novel applications of these data emerge. The unique characteristics of these data provide many opportunities to improve existing workflows and processing methods in a wide range of terrain-related fields of study. For example, LiDAR data have been used for soils mapping, forest canopy modelling, stream mapping, sediment erosion modelling, solar potential modelling, and many other applications involving accurate topographic and canopy modelling. However, the properties of LiDAR data also present numerous and significant challenges for end-users and at present practitioners are commonly struggling to take full advantage of their LiDAR data sets. In addition to the technical issues associated with managing large data volumes, researchers and practitioners are also commonly confronted with problems associated with the extremely fine detail of surface representation. For instance, LiDAR DEMs often include microtopography and excessive surface roughness that can complicate the measurement of the surface parameters (e.g. slope, orientation, curvature, topographic position, surface flow) that are common inputs for other upstream modelling workflows. This presentation will introduce potential solutions to some of these issues, as well as describe their role in enabling large-scale applications of these unique data.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,027 | 0,077 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,006 | 0,009 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,008 | 0,020 |
| Science ouverte | 0,008 | 0,012 |
| Intégrité de la recherche | 0,004 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».