The monitoring of tree species diversity is important for forest or wetland ecosystem service maintenance or resource management. Remote sensing is an efficient alternative to traditional field work to map tree species diversity over large areas. Previous studies have used light detection and ranging (LiDAR) and imaging spectroscopy (hyperspectral or multispectral remote sensing) for species richness prediction. The recent development of very high spatial resolution (VHR) RGB images has enabled detailed characterization of canopies and forest structures. In this study, we developed a three-step workflow for mapping tree species diversity, the aim of which was to increase knowledge of tree species diversity assessment using deep learning in a tropical wetland (Haizhu Wetland) in South China based on VHR-RGB images and LiDAR points. Firstly, individual trees were detected based on a canopy height model (CHM, derived from LiDAR points) by the local-maxima-based method in the FUSION software (Version 3.70, Seattle, USA). Then, tree species at the individual tree level were identified via a patch-based image input method, which cropped the RGB images into small patches (the individually detected trees) based on the tree apexes detected. Three different deep learning methods (i.e., AlexNet, VGG16, and ResNet50) were modified to classify the tree species, as they can make good use of the spatial context information. Finally, four diversity indices, namely, the Margalef richness index, the Shannon–Wiener diversity index, the Simpson diversity index, and the Pielou evenness index, were calculated from the fixed subset with a size of 30 × 30 m for assessment. In the classification phase, VGG16 had the best performance, with an overall accuracy of 73.25% for 18 tree species. Based on the classification results, mapping of tree species diversity showed reasonable agreement with field survey data (R²_Margalef = 0.4562, root-mean-square error RMSE_Margalef = 0.5629; R²_{Shannon–Wiener} = 0.7948, RMSE_{Shannon–Wiener} = 0.7202; R²_Simpson = 0.7907, RMSE_Simpson = 0.1038; and R²_Pielou = 0.5875, RMSE_Pielou = 0.3053). While challenges remain for individual tree detection and species classification, the deep-learning-based solution shows potential for mapping tree species diversity.

Building change detection from very high-spatial-resolution (VHR) remote sensing images has gained increasing popularity in a variety of applications, such as urban planning and damage assessment. Detecting fine-grained ``from-to'' changes (change transition from one land cover type to another) of buildings from the VHR images is still challenging as multitemporal representation is complicated. Recently, fully convolutional neural networks (FCNs) have been proven to be capable of feature extraction and semantic segmentation of VHR images, but its ability in change detection is untested and unknown. In this letter, we leverage the semantic segmentation of buildings as an auxiliary source of information for the fine-grained ``from-to'' change detection. A deep multitask learning framework for change detection (MTL-CD) is proposed for detecting building changes from the VHR images. MTL-CD adopts the encoder-decoder architecture and solves the main task of change detection and the auxiliary tasks of semantic segmentation simultaneously. Accordingly, the change detection loss function is constrained by the auxiliary semantic segmentation tasks and enables the back-propagation of the building footprints' detection errors for the improvement of change detection. A building change detection data set named the Guangzhou data set is also developed for model evaluation, in which the bitemporal R-G-B images were collected by airplane (2009) and unmanned aerial vehicle (UAV, 2019) with different flight heights. Experiments on the Guangzhou data set demonstrate that the MTL-CD method effectively detects fine-grained ``from-to'' changes and outperforms the postclassification methods and the direct change detection methods.

The recent applications of fully convolutional networks (FCNs) have shown to improve the semantic segmentation of very high resolution (VHR) remote-sensing images because of the excellent feature representation and end-to-end pixel labeling capabilities. While many FCN-based methods concatenate features from multilevel encoding stages to refine the coarse labeling results, the semantic gap between features of different levels and the selection of representative features are often overlooked, leading to the generation of redundant information and unexpected classification results. In this article, we propose an attention-guided label refinement network (ALRNet) for improved semantic labeling of VHR images. ALRNet follows the paradigm of the encoder-decoder architecture, which progressively refines the coarse labeling maps of different scales by using the channelwise attention mechanism. A novel attention-guided feature fusion module based on the squeeze-and-excitation module is designed to fuse higher level and lower level features. In this way, the semantic gaps among features of different levels are declined, and the category discrimination of each pixel in the lower level features is strengthened, which is helpful for subsequent label refinement. ALRNet is tested on three public datasets, including two ISRPS 2-D labeling datasets and the Wuhan University aerial building dataset. Results demonstrated that ALRNet had shown promising segmentation performance in comparison with state-of-the-art deep learning networks. The source code of ALRNet is made publicly available for further studies.