###### Abstract  
Timely information about the need to thin forests is vital in forest management to maintain a healthy forest while maximizing income. Currently, very-high-spatial-resolution remote sensing data can provide crucial assistance to experts when evaluating the maturity of thinnings. Nevertheless, this task is still predominantly carried out in the field and demands extensive resources. This paper presents a deep convolutional neural network (DCNN) to detect the necessity and urgency of carrying out thinnings using only remote sensing data. The approach uses very-high-spatial-resolution RGB and near-infrared orthophotos; a canopy height model (CHM); a digital terrain model (DTM); the slope; and reference data, which, in this case, originate from spruce-dominated forests in the Austrian Alps. After tuning, the model achieves an F1 score of 82.23% on our test data, which indicates that the model is usable in a practical setting. We conclude that DCNNs are capable of detecting the need to carry out thinnings in forests. In contrast, attempts to assess the urgency of the need for thinnings with DCNNs proved to be unsuccessful. However, additional data, such as age or yield class, have the potential to improve the results. Our investigation into the influence of each individual input feature shows that orthophotos appear to contain the most relevant information for detecting the need for thinning. Moreover, we observe a gain in performance when adding the CHM and slope, whereas adding the DTM harms the model's performance. forest management; agroforestry; computer vision; deep learning; thinning Application to the Lungau Region, Austria. 30 July 20231  
over large areas [8]. Furthermore, with recent advances in sensor technology as well as computing power, there has been increased interest in applying algorithms for the automated extraction of forest parameters from remotely sensed spectral information [9]. Aerial forestry data coupled with recent increases in computing power allow machine learning (ML) algorithms to be deployed for applications such as detecting changes in forests [10; 11], classifying tree species [12; 13; 14; 15], and estimating stand volume [16; 17; 18]. Despite the high level of importance of the timely planning of forest operations, few studies have been conducted to predict the necessity of thinning from remote sensing data. There are only two known studies that addressed this challenge. First, ref. [19] tried to predict forest operations at the stand level using Landsat satellite imagery with moderate success. Then, ref. [20] employed ALS data-derived features to predict thinning maturity at the stand level. This study showed much better results, with classification accuracy ranging from 79% to 83% when predicting the timing of the subsequent thinning. Another approach to predicting the need for thinning is to utilize key forest parameters acquired through remote sensing, together with additional inventory data, to create statistical models [21]. Nevertheless, ALS data are still expensive to obtain, and only a few countries acquire it systematically. We used semantic segmentation to extract information about the need for thinning from widespread areal images as this method was successfully used for many remote sensing tasks [22]. Semantic segmentation is a computer vision task where the algorithm labels each pixel or patch of an image according to a predefined range of classes. The utilization of end-to-end DCNNs for the task of semantic segmentation has increased greatly and was accelerated via the first end-to-end fully convolutional network (FCN) [23]. Since the invention of this network type, many different architectures have been proposed and adapted to remote sensing applications [24; 25; 26]. The main advantage of using DCNNs for semantic segmentation is their effectiveness in extracting complex features from wide receptive fields.