###### Abstract
Nonetheless, this capability comes with the price of not being able to maintain a high spatial resolution and results in inaccurate and blurred boundaries between the classes. To counteract this, newer DCNNs use more pronounced or distinct encoder-decoder architectures with skip connections. For example, UNet applies such skip connections in its symmetrical encoder-decoder architecture [27], where the features extracted in the encoder are directly coupled to the corresponding decoder layers. The outstanding performance of this architecture, together with its simplicity, has ensured its widespread adoption in the remote sensing research community for a variety of applications, such as ship detection [28], road extraction [29], and land cover classification [30]. Deeper DCNNs with more stacked layers were created to obtain models that can learn more complex input representations. However, these deeper DCNNs often resulted in worse-performing models than their more shallow predecessors due to the degradation problem [31]. This issue was resolved using deep residual networks (e.g., ResNet) that employed residual blocks, which added an identity shortcut connection and overcame the degradation problem [32]. The further development of DCNNs resulted in the creation of a network architecture called DenseNet, which utilizes dense blocks that introduce direct connections from any layer to all subsequent layers to further improve the flow of information between layers [33]. [PERSON] et al. [34] adopted the DenseNet connection structure for semantic segmentation by applying dense blocks to a UNet-like symmetric encoder-decoder structure. This merge resulted in a DCNN called FC-DenseNet, which required fewer input parameters while performing better on various semantic segmentation challenges. Another compelling approach to resolving the trade-off between high-context extraction with heavy downsampling and accurate boundary prediction is the use of dilated or atrous convolutions. Atrous convolutions utilize a convolution filter that is spaced apart, thus allowing them to attain a wider field of view while retaining their spatial dimensions. Ref. [35] proposed a DCNN architecture called DeepLabv1 that incorporates atrous convolutions into a VGG-16 architecture to address the trade-off between high-context extraction and heavy downsampling. Several revisions to this architecture occurred, including, among other things, the incorporation of the spatial pyramidal pooling method that was introduced in SPPNet [36] and adopted by [37] to create the atrous spatial pyramid pooling (ASPP) method used in DeepLabv2. The current network architecture, DeepLabv3+ [38], provides some of the best results for semantic segmentation challenges. Furthermore, its use in remote sensing applications is auspicious. For example, ref. [39] showed the DeepLabv3+ network's excellent performance in classifying marsh vegetation in China. Accordingly, DeepLabv3+ is the network used in Section 3.2 to classify regions in need of tree thinning. Nevertheless, little research has been performed to detect the necessity of thinning solely with remote sensing data. This paper presents a model capable of achieving this task. In this study, we tested a deep learning approach to determine if remote sensing data alone could be used to detect the need for thinning. The two factors investigated were whether the approach could  
1. Detect if thinning is needed, based on deep convolutional neural networks (DCNNs) trained on very high spatial resolution imagery;
2. Differentiate between thinnings with different urgencies (i.e., timescales). An accurate prediction of thinnings from remote sensing data has the potential to deliver information for vast or remote forest areas promptly, thus improving the stability and wood quality of forests.