Application of Remote Sensing Imagery and Ancillary Products to Improve Safety and Logistical Efficiency of Arctic Operations
Notice bibliographique
Résumé
Remote Sensing Imagery and the derived ancillary products improved the efficiency and safety of upstream oil and gas operations on the North Slope of Alaska. These Arctic regions are remote, very difficult to access in general and sometimes only seasonably accessible. Our prudent and responsible Arctic Operations require regional-reconnaissance exploration, diligent monitoring of environment such as current state of vegetation, temporal changes of terrain, water drainage system and lakes. Finally, we also need very detailed logistical-planning of field operations. Remote sensing imagery and its derived ancillary products demonstrably improved all these aspects of our Arctic Operations. For Arctic Operations, remote sensing data consisted of optical satellite and aerial imagery at various spectral and spatial resolutions, high resolution LIDAR data for digital elevation and digital surface models and synthetic aperture radar imagery (SAR). A combination of in-house and commercial software was used to ingest and process these data. The optical imagery was processed and enhanced using various spectral combinations and high pass filtering to generate the highest possible spatial-resolution for each sensor. Classic neural networks analysis was used to classify the optical imagery for vegetation. The SAR imagery was calibrated (for all polarizations) and geometrically corrected to remove layover effects. The processed optical and SAR imagery, LIDAR and ancillary products were co-registered and imported into a GIS system for final analysis and applications. The optical imagery provided information about surface feature such as lake outlines, general drainage, active channels in Colville River, general lake ice conditions, classification of vegetation types etc. The LIDAR data were used to generate slope maps (for arctic vehicles), general topographic conditions and field operations. The SAR imagery was used to monitor surface conditions when optical imagery was not available during the Arctic night conditions. SAR imagery was also used to calculate the ice thickness proxy maps for eventual field operations. All of these products contributed directly to our environmental baseline studies, improved our field operation efficiency and general safety of our Arctic Operations. For a practicing engineer (individual or team) The remote sensing data and derived products for Arctic Operations were made available via GIS system. This allowed easy integration with other data layers as well as a common background for all different disciplines to monitor progress and to contribute their learnings and ideas to the entire team.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».