Influence of natural factors on the accuracy of cartographic visualization of space-time structures of automobile roads of Ukraine
Notice bibliographique
Résumé
The purpose of the article is to analyze the influence of the main natural factors, topography, hydrographic conditions, climatic (weather) phenomena and vegetation on the accuracy of cartographic visualization of highways in Ukraine. Main material. The concept of accuracy of cartographic visualization is considered in a wide range of factors - not only as geometric accuracy of visualization, but also as completeness, timeliness/efficiency and reliability of information. The article presents a comparative analysis of the content and conventional designations of highways in regulatory documents of Europe, the USA, Canada and Ukraine. It was found that the means of representation on maps of different countries are similar to each other, easily identifiable and similar in parameters. At the same time, it was found that not only traditional, but also electronic navigation maps of highways presented by Google Maps and OpenStreetMap services lack information about natural landscapes and their components (relief, hydrographic conditions, vegetation), as well as about weather and climatic conditions that have a direct impact on the accuracy of cartographic visualization. The main parameters that should be taken into account when creating navigation maps and highway maps in Ukraine are mentioned. Conclusions. Natural factors play a significant role in shaping the spatiotemporal structures of highways and traffic conditions on them. The study allows us to draw the following conclusions: 1. The traditional understanding of map accuracy (as metric accuracy) is narrow for modern navigation needs; 2. Natural factors (relief, hydrography, weather and climatic conditions, vegetation) significantly affect the information content of road maps; 3. To increase the accuracy of maps, it is necessary to integrate digital terrain models into navigation systems and apply high-tech survey methods (for example, LiDAR) to update elevation data; 4. Ukrainian professional institutions should, through cooperation between the State Emergency Service and map developers, develop solutions for reflecting the threat of floods and roadway flooding; 5. Integrating real-world data on hazardous weather conditions and phenomena into maps will increase their value for drivers and can prevent many accidents, as confirmed by both scientific research and the practice of road services abroad; 6. The display of vegetation cover, as one of the hazardous natural factors, is not taken into account on digital maps, and therefore it is necessary to develop methods for its accounting - from adding relevant attributes to databases to using crowdsourced reports about thickets or fallen trees; 7. The lack of regulation of the integration of natural factors into cartographic systems hinders their implementation. A scientifically based standard (guidelines, instructions) is needed that will determine what natural data should be present in electronic road maps and with what frequency they should be updated. Establishing such requirements at the level of State Standards and departmental instructions will ensure a systematic approach and compatibility of data from different services.
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 | 0,001 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».