Self-Organizing Deep Learning (SO-UNet)—A Novel Framework to Classify Urban and Peri-Urban Forests

Tree species classification of LiDAR data based on 3D deep learning

(2021) Maohua Liu et al.   MEASUREMENT

An Improved Res-UNet Model for Tree Species Classification Using Airborne High-Resolution Images

(2020) Kaili Cao et al.   Remote Sensing

An Analysis of Fast Learning Methods for Classifying Forest Cover Types

(2020) Hugo Sjöqvist et al.   APPLIED ARTIFICIAL INTELLIGENCE

Mapping dead forest cover using a deep convolutional neural network and digital aerial photography

(2019) Jean-Daniel Sylvain et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft

(2018) Roope Näsi et al.   URBAN FORESTRY & URBAN GREENING

Learning and Transferring Deep Joint Spectral–Spatial Features for Hyperspectral Classification

(2017) Jingxiang Yang et al.   IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

(2017) Angela Lausch et al.   Remote Sensing

Forest mapping: a comparison between hyperspectral and multispectral images and technologies

(2017) Mohamad M. Awad   JOURNAL OF FORESTRY RESEARCH

Use of UAV oblique imaging for the detection of individual trees in residential environments

(2015) Yi Lin et al.   URBAN FORESTRY & URBAN GREENING

A framework for developing urban forest ecosystem services and goods indicators

(2010) Cynnamon Dobbs et al.   LANDSCAPE AND URBAN PLANNING