## 1 Introduction
Secondly, individual features struggle to fully capture the complexity and diversity of land cover types [12], thus reducing classifier performance. Additionally, researchers frequently neglect the interactions among features of surface objects and nonlinear relationships during the process of feature extraction [13], leading to limitations in the classification outcomes. To enhance the accuracy of land cover classification further, researchers have started to concentrate on methods that incorporate the fusion of multi-source data and combinations of multiple features to address the limitations of individual features in classification [14]. In the domain of deep learning classification, [PERSON] et al. [15] proposed a multi-scale 3D convolutional neural network model. This model intelligently combines spectral and spatial features extracted from remote sensing images in an end-to-end manner, thereby improving classification accuracy. To mitigate computational costs, [PERSON] et al. proposed a preprocessingfree classification model based on 3D convolutional neural networks (3 DCNN), which directly input hyperspectral images into the 3 DCNN [16]. [PERSON] et al. [17] decreased the band dimension of HRSI before using 3 DCNN to extract features. However, this approach led to the loss of spectral continuity in image data, affecting classification accuracy. [PERSON] et al. [18] and [PERSON] et al. [19] classified the spectral-spatial features obtained from the 3 DCNN model. [PERSON] et al. [20] also combined 3 DCNN with 2 DCNN, where 3 DCNN first extracted spectral-spatial features, followed by refinement by 2 DCNN [21]. Recently, the field of remote sensing classification has widely embraced ensemble classification methods. [PERSON] et al. [22] developed five random forest ensemble models, including bagging-based, boosting-based, random subspace (RS)-based, rotation-based, and boosted-based approaches. These approaches effectively enhanced the prediction accuracy of the random forest ensemble system. [PERSON] et al. [23] utilized convolutional neural networks (CNNs) and deep residual networks (ResNets) as base classifiers, advancing the development of ensemble learning through a deep learning-based random subspace ensemble framework. [PERSON] et al. [24] have achieved excellent recognition performance by combining multiple collaborative representations with boosting techniques. This study focuses on two typical regions in Czech, namely the South and East. It aims to address the shortcomings in feature selection methods in current land cover classification approaches for Czech, resulting in poor classification accuracy and effectiveness. We propose the utilization of 3 DCNN deep learning networks for feature extraction and ensemble learning methods for land cover classification to address the challenge of acquiring spatial distribution data for forest resources. The research data consist of Sentinel-2 satellite images and DEM data, which are utilized for extracting features, including spectral, vegetation, and terrain features. Calculations for texture and morphological features are planned to enhance the utilization of spectral information in images. Additionally, the use of 3 DCNN networks for extracting deep semantic information from images enables more effective differentiation of various land cover categories. Finally, these features are input into ensemble learning classifiers to investigate classification performance. This study can serve as a reference for research methods in deep learning for land cover classification, utilizing optimized features. The main contributions and innovations of this study are as follows:  
1. This study explores the importance of feature engineering and optimization in Czech land cover classification. Initially, this study extracts multidimensional features from remote sensing images of the experimental area, encompassing spectral, texture, morphological, and topographical features. The importance of the extracted features is compared experimentally to make the final optimization.