Step-wise Land-class Elimination Approach for extracting mixed-type built-up areas of Kolkata megacity
Notice bibliographique
Résumé
The extraction of urban built-up areas is an important aspect of urban planning and understanding the complex drivers and biophysical mechanism of urban climate processes. However, built-up area extraction using Landsat data is a challenging task due to spatio-temporal dynamics and spatially intermixed nature of Land Use and Land Cover (LULC) in the cities of the developing countries, particularly in tropics. In the light of advantages and drawbacks of the Normalized Difference Built-up Index (NDBI) and Built-up Area Extraction Method (BAEM), a new and simple method i.e. Step-wise Land-class Elimination Approach (SLEA) is proposed for rapid and accurate mapping of urban built-up areas without depending exclusively on the band specific normalized indices, in order to pursue a more generalized approach. It combines the use of a single band layer, Normalized Difference Vegetation Index (NDVI) image and another binary image obtained through Logit model. Based on the spectral designation of the satellite image in use, a particular band is chosen for identification of water pixels. The Double-window Flexible Pace Search (DFPS) approach is employed for finding the optimum threshold value that segments the selected band image into water and non-water categories. The water pixels are then eliminated from the original image. The vegetation pixels are similarly identified using the NDVI image and eliminated. The residual pixels left after elimination of water and vegetation categories belong either to the built-up areas or to bare land categories. Logit model is used for separation of the built-up areas from bare lands. The effectiveness of this method was tested through the mapping of built-up areas of the Kolkata Metropolitan Area (KMA), India from Thematic Mapper (TM) images of 2000, 2005 and 2010, and Operational Land Imager (OLI) image of 2015. Results of the proposed SLEA were 95.33% accurate on the whole, while those derived by the NDBI and BAEM approaches returned an overall accuracy of 83.67% and 89.33%, respectively. Comparisons of the results obtained using this method with those obtained from NDBI and BAEM approaches demonstrate that the proposed approach is quite reliable. The SLEA generates new patterns of evidence and hypotheses for built-up areas extraction research, providing an integral link with statistical science and encouraging trans-disciplinary collaborations to build robust knowledge and problem solving capacity in urban areas. It also brings landscape architecture, urban and regional planning, landscape and ecological engineering, and other practice-oriented fields to bear together in processes for identifying problems and analyzing, synthesizng, and evaluating desirable alternatives for urban change. This method produced very accurate results in a more efficient manner compared to the earlier built-up area extraction approaches for the landscape and urban planning.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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) | 0,002 | 0,001 |
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 ».