Bamfield Region UAV Imagery and Surface Model Data
Notice bibliographique
Résumé
Bamfield UAV Data Capture April 9 & 10th 2016. Hakai Institute: Iain McKechnie, Will McInnes, and Keith Holmes. The Hakai Institute in partnership with the Bamfield Marine Science Centre (BMSC) have conducted UAV (unmanned aerial vehicle) missions in five locations around Bamfield, British Columbia, Canada. The missions took place April 9th and 10th, 2016. The main goal was to collect high spatial resolution imagery and surface models of key research areas for BMSC researchers. All UAV flights and data processing has been completed by the Hakai Institute and the aim is to collaborate research efforts to enhance the spatial knowledge of these key research areas. The low tide windows between 8:30 and 11:30 am on April 9th and 10th provided imagery with tides of .5 meters or less and image resolutions of between 3cm and 10cm. Weather was mixed and we had to improvise flights to compensate for low level clouds and fog. In the end we were able to accomplish all our major target areas. Data products that can be generated are the following: High resolution imagery mosaics Digital surface models LAS point cloud data 3D mesh data Raw imagery and video footage Georeferencing points and 2 survey markers for Diana Island Methodology: 1) Georeferencing targets were distributed throughout the image capture area. Targets are checkered black and white and 60cm x 60cm. 2) Phantom 3 Pro UAVs were used for all imagery acquisition. Flights were conducted with a grid pattern with greater than 50% overlap in imagery. Small area flights required less images like Wizard with 237 images processed and larger areas like Grappler requiring 504 images. Flights take roughly 15 minutes to conduct and cover up to 1.5 x 1.5 km in a single flight. Full specs for image capture are listed below. 3) After the flights are conducted the field team inspects the survey coverage using the DJI GO applicaton map. Once they are satisfied a georeferencing mission starts. Georeferencing requires the use of a Topcon DGPS GR5 survey grade GPS system. Targets placed before the flight and opportunistic targets (discussed later) are referenced to within 10 cm horizontal / 15 cm vertical resolution. DGPS soaking is used to enhance accuracy which takes anywhere from 15 seconds to 3 minutes to achieve acceptable levels of variance. Many of the targets achieved well below these numbers. It is essential that the targets are well dispersed. If some areas have not been covered well with targets or there was limited time pre flight to dispense targets opportunistic targets are used. Opportunistic targets are large stationary objects that are close to the ground and can easily be identified in imagery. Often driftwood or geographic features like large rocks or fissures are ideal in the field if no man made structures like cement pads are available. 4) Image processing is conducted using Pix4D software. Images are first inspected for quality (remove high glare or poor contrast images) and then the full data process takes place which includes a flight report, ray cloud / image tie points, triangulated mesh, mosaic and surface model. 5) Image mosaics and digital surface models are then processed in ArcGIS Desktop. Survey DGPS points from the field are used to link up the targets and a sline correction method is used because it assumes we have highly accurate survey data to correct the imagery with. The georeferencing pins are saved and processed again with the accompanying surface model. The final datasets are then exported to our servers for archiving.
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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,001 | 0,009 |
| Science ouverte | 0,007 | 0,011 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,022 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».