Tree Ecosystem Services Analysis Platform
Notice bibliographique
Résumé
As UBC Electrical and Computer Engineering Capstone Team 117, we worked in collaboration with UBC Campus and Community Planning and UBC SEEDS to develop a project to help support sustainable development policies and goals at UBC. Our project, the Tree Ecosystem Services Analysis Platform (TREESAP), is a tool for analysing and visualizing ecosystem services on the UBC Vancouver campus. We have built a processing pipeline that extracts locations of tree cover on campus from aerial scans, which are in LiDAR and orthophoto formats. Tree clusters are identified in orthophoto by using colour and standard deviation to segment and separate trees from other areas. LiDAR processing consists of a 3D Density-Based Spatial Clustering (DBSCAN) clustering algorithm to identify and separate tree clusters from other points in the scan. These clusters are then smoothed using alpha shape to obtain the optimal outer contour for the cluster. We also provide a desktop app to visualize this data, allowing users to generate reports about ecosystem services, in particular, the carbon sequestration in tonnes/year and the avoided rainwater runoff in litres/year that trees provide. Additionally, shading and cooling information can be gathered by using the interface to calculate the distance between buildings and trees. This app can be used to plan future developments by seeing the potential effects of adding and removing trees. TREESAP provides baseline data for ecosystem services on campus, helping urban planners develop quantitative arboreal sustainability goals. The deliverables of this project are explained in more detail in Appendix 1. Given the limited timeframe of our project, we designed the system with future extensions in mind. We’ve come up with several ideas that future teams could implement: ● Modify or create a new data processing pipeline to use new data sources or new approaches (e.g. machine learning) to provide more or better data. The input to the desktop application is polygons of GeoJson format, so any processing technique that produces valid GeoJson can make use of the desktop application ● Add economic value of the ecosystem services to the desktop application. For example, trees near a cliff may be much more valuable for avoided runoff than those in the middle of campus ● Add more ecosystem services to the desktop application. iTree Canopy1 provides many more ecosystem benefits that depend only on tree cover area, so adding these would work within the architecture of our desktop application. ● Update and scale desktop app so it can be publicly accessible as a tool for public engagement. This may involve some service fees for Google Maps API, for example, on top of usability improvements. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”
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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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,056 | 0,039 |
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 ».