Applications of GeoAI in Extracting National Value-Added Products from Historical Airborne Photography
Notice bibliographique
Résumé
Canadian national air photo library (NAPL) comprises millions of historical airborne photographs dating over 100 years. Historical photographs are rich chronicles of countrywide geospatial information. They can be used for creating long-term time series and supporting various analytics such as monitoring expansion/shrinking rates of built areas, forest structure change measurement, measuring thinning and retreating rates of glaciers, and determining rates of erosion at coastlines. Various technical solutions are developed at Natural Resources Canada (NRCan) to generate analysis-ready mapping products from NAPL.Photogrammetric Processing with a Focus on Automated Georeferencing of Historical Photos: The main technical challenge of photogrammetric processing is identifying reference observations, such as ground control points (GCP). Reference observations are crucial to accurately georeference historical photos and ensure the spatial alignment of historical and modern mapping products. This is critical for creating time series and performing multi-temporal change analytics. In our workflow, GCPs are identified by automatically matching historical images to modern optical satellite/airborne ortho-rectified images. In the matching process, first, we use convolutional neural networks (D2Net) for joint feature detection and description in the intensity space. Then, we convert intensity images to phase congruency maps, which show less sensitivity to nonlinear radiometric differences of the images, and we extract an additional set of features using the Fast detector and describe them using the radiation-invariant feature transform (RIFT). Feature-matching outliers are detected and removed via random sample consensus (Ransac), enforcing a homographic transformation between corresponding images. The remaining control points are manually verified through a graphical interface built as a QGIS plugin. The verified control points are then used in a bundle block adjustment, where external orientation parameters of the historical images and the intrinsic calibration parameters of the cameras are refined, followed by dense matching and generation of digital elevation models and ortho-rectified mosaics using conventional photogrammetric approaches. These solutions are implemented using our in-house libraries as well as MicMac open-source software. Through the presentation, examples of the generated products and their qualities will be demonstrated.Deep Colourization, Super Resolution and Semantic Segmentation: Considering the fact that NAPL mostly contains grayscale photos, their visual appeal and interpretability are less than modern colour images. In addition, the automated extraction of colour-sensitive features from them, e.g. water bodies, is more complicated than colour images. With this regard, we have developed fully automated approaches to colourize historical ortho-rectified mosaics based on image-to-image translation models. Through the presentation, the performance of a variety of solutions like conditional generative adversarial networks (GAN), encoder-decoder networks, vision transformers, and probabilistic diffusion models will be compared. In addition, using a customized GAN, we improve the spatial resolution of historical images which are scanned from printed photos at low resolution (as opposed to being scanned directly from film rolls at high resolution). Our semantic segmentation models, trained initially on optical satellite and airborne imagery, are also adapted to historical air photos for extracting water bodies, road networks, building outlines, and forested areas. The performance of these models on historical photos will be demonstrated during the presentation.
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 | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».