Procedural vegetation generator algorithm with L-System developed in Javascript using P5.js
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Updated
Apr 8, 2022 - JavaScript
Procedural vegetation generator algorithm with L-System developed in Javascript using P5.js
Chesapeake Bay SAV Data collection via http://www.vims.edu/ research team. More than a spatial, and tabular data repository, the older .arc files have been converted to shapefiles, all have been converted to geojson. Future intentions include visualizing sav annual maps over time (70s-current), real-time analysis and monitoring, as well as some …
[doi: 10.12202/j.0476-0301.2021171] Hui Zhang, Cenliang Zhao, Wenquan Zhu*. A new vegetation map for Qinghai-Tibet Plateau by integrated classification from multi-source data products. Journal of Beijing Normal University (Natural Science), 2021.
Continuous foliar cover maps of vegetation species and aggregates for North American Beringia (arctic and boreal Alaska and Yukon).
Object detection work done for the OpenOrbiter REU program at UND. See website for full work
Prediction of vegetation coverage maps from High Density Lidar data, in a weakly supervised deep learning setting.
Repository for the research project "PhenomEn" (Swiss National Science Foundation) - PhD thesis Lukas Valentin Graf
Derive Vegetation Characteristics from Drone imagery
List of RBB Geospatial Data Catalogue in Sharepoint
Supplementary material to the Master's thesis "Assessing the Impact of TLS-Derived Vegetation Structure on Microclimatic Variability in Taita Hills, Kenya" by Jonathan Terschanski.
[doi: 10.11834/jrs.20211394] Cenliang Zhao, Wenquan Zhu*, Zhiying Xie. Comparative evaluation of simulation methods for vegetation maximum light use efficiency. National Remote Sensing Bulletin, 2021.
Webová aplikácia postavená na mikrokontroléri Arduino Mega 2560 s Ethernet konektivitou do webového rozhrania slúžiaceho na správu celého projektu
codes for JSTARS paper: Sentinel-3/FLEX Biophysical Product Confidence Using Sentinel-2 Land-Cover Spatial Distributions
Biodiversity data of the Nederlands Kruidkundig Archief
Student project for plant recognition and inventory over position and time
Detecting vegetation anomalies from CGLS products (e.g. NDVI)
The PiCam time-lapse camera system. A nRF52840 managed, low power sensor node and Raspberry Pi camera system designed for autonomous operations in remote regions for the collection of vegetation phenology data
Auto-encoder for vegetation classification.
A rapid floodfill tool for foliage textures.
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