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3d-forest-classic

software for analysis of Lidar data from forest environment.

aerial-image-segmentation

Aerial Image segmentation by PyTorch

aerial-lidar-classification

allometree

allometree: Allometric scaling of urban trees

an-unexpectedly-large-count-of-trees

This repository contains the code for the paper "An unexpectedly large count of trees in the western Sahara and Sahel".

arborizer

Tree species segmentation and classification algorithm for SwissimageRS 2018 (Swisstopo)

awesome-forests

🌳 A curated list of ground-truth forest datasets for the machine learning and forestry community.

awesome-remote-sensing-change-detection

List of datasets, codes, and contests related to remote sensing change detection

awesome-satellite-imagery-datasets

🛰️ List of satellite image training datasets with annotations for computer vision and deep learning

aws-vegetation-management-workshop

Leveraging deep learning on satellite images and LiDAR data using AWS machine learning services can identify areas of risk. Utility companies can use the identified anomalies to monitor vegetation and proactively intervene to prevent wildfires and protect critical infrastructure. In this workshop, learn how to use Amazon SageMaker to process satellite images and LiDAR data and identify vegetation risks using deep learning.

barkbeetle-damage-classification-dl

chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://arxiv.org/pdf/2207.07241v1.pdf

bayts

Set of tools to apply the probabilistic approach of Reiche et al. (2015, 2018) to combine multiple optical and/or Radar satellite time series and to detect deforestation/forest cover loss in near real-time. The package includes functions to apply the approach to both, single pixel time series and raster time series.

canoclass

Open source canopy classification system

canopy

Automatic tree species classification from remote sensing data

canopy-height-model-with-lidar-data

Creation and extraction of CHM (tree heights) with LiDAR

chm_from_lidar

The CHM from LIDAR plugin creates a Canopy Height Model (CHM) starting from LIDAR data (DTM and DSM First Pulse)

comparison-of-high-resolution-global-canopy-height-maps-and-their-applicability

Global Canopy Height Model Comparison in R

counting-trees-using-satellite-images

This study investigates the aspect of localizing and counting trees using satellite images to create an inventory of incoming and outgoing trees for an annual tree inspections.

deepforest

Python Package for Tree Crown Detection in Airborne RGB imagery

deeptreeattention

Implementation of Hang et al. 2020 "Hyperspectral Image Classification with Attention Aided CNNs" for tree species prediction

detectree

Tree detection from aerial imagery in Python

ea_lidar_download

Python code to bulk download UK Environment Agency LiDAR data

environmental-ai-book

This is a deprecated repository of the Environmental AI book (version 0.0.1). The repository is currently maintained in https://github.com/alan-turing-institute/environmental-ds-book

eo-learn

Earth observation processing framework for machine learning in Python

forest-cover-type-using-deep-learning

Predicting forest cover type from cartographic variables only (no remotely sensed data). The actual forest cover type for a given observation (30 x 30 meter cell) was determined from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. Independent variables were derived from data originally obtained from US Geological Survey (USGS) and USFS data. Data is in raw form (not scaled) and contains binary (0 or 1) columns of data for qualitative independent variables (wilderness areas and soil types). This study area includes four wilderness areas located in the Roosevelt National Forest of northern Colorado. These areas represent forests with minimal human-caused disturbances, so that existing forest cover types are more a result of ecological processes rather than forest management practices. Some background information for these four wilderness areas: Neota (area 2) probably has the highest mean elevational value of the 4 wilderness areas. Rawah (area 1) and Comanche Peak (area 3) would have a lower mean elevational value, while Cache la Poudre (area 4) would have the lowest mean elevational value. As for primary major tree species in these areas, Neota would have spruce/fir (type 1), while Rawah and Comanche Peak would probably have lodgepole pine (type 2) as their primary species, followed by spruce/fir and aspen (type 5). Cache la Poudre would tend to have Ponderosa pine (type 3), Douglas-fir (type 6), and cottonwood/willow (type 4). The Rawah and Comanche Peak areas would tend to be more typical of the overall dataset than either the Neota or Cache la Poudre, due to their assortment of tree species and range of predictive variable values (elevation, etc.) Cache la Poudre would probably be more unique than the others, due to its relatively low elevation range and species composition.

forest_cover-type

Classifying Forest Cover type from OTHER DATA eg topo, shadow, sunlight etc

forestedwetlands

Code for building raster files of elevation derivatives, python and R scripts for using these rasters for building and applying random forest models of wetland presence/absence in ArcGIS Pro

forestlinemapper-roadsandgaps

Forest Line Mapper: Series of script tools for facilitating the high-resolution mapping and studying of forest lines via processing canopy height models.

forestmetrics

Individual tree segmentation from LiDAR-derived point clouds

foresttools

Detect and segment individual tree from remotely sensed data