###### Abstract
This dataset is meticulously annotated at the pixel level and categorizes features into 11 distinct types, such as debris, water, buildings, vehicles, roads, trees, ponds, and sand. Moreover, RescueNet includes four unique labels for segmenting buildings based on varying degrees of damage. [PERSON] [9] proposed a network that considers heterogeneous features of damaged buildings. This network utilizes a local-global context attention module, which extracts features from multiple directions. The test results indicated that compared with excellent deep learning models, the proposed method achieved a joint intersection-over-union (IOU) growth of 0.03-7.39%. [PERSON] [10] developed an end-to-end model for building segmentation that integrates a unique location-aware loss function. This function combines binary cross-entropy loss with foreground-selective category cross-entropy loss to classify damage. This model outperforms those utilizing conventional cross-entropy loss in terms of building segmentation and damage classification. Additionally, it demonstrates enhanced generalization across diverse geographical regions and types of disasters. [PERSON] [11] proposed a two-stage CNN designed for assessing building damage. Initially, a U-Net is employed to identify building locations. Subsequently, the second stage utilizes a dual-branch, multi-scale U-Net architecture as its core framework. Pre-disaster and post-disaster images were input into the network, and a cross-directional attention module was employed to explore correlations among these images. [PERSON] [12] developed the ChangeOS framework for semantic change detection, utilizing a deep object localization network to accurately identify building structures for damage assessment. Comparative studies demonstrated that ChangeOS outperformed existing methods in terms of speed and accuracy, also showing enhanced generalization capabilities for anthropogenic disasters. [PERSON] [13] proposed a new deep learning algorithm that replaces the upsampling layer in U-Net3+ with a sub-pixel count convolutional layer, thus improving the problem of poor segmentation including irrelevant change information and inconsistent boundaries present in building change detection. [PERSON] [14] suggested employing a U-net convolutional network for the semantic segmentation of building damage in high-resolution remote sensing imagery. The effectiveness of the U-Net model was evaluated by comparing it with the deep residual U-Net model, with the 2011 Tohoku earthquake tsunami serving as a benchmark. [PERSON] [15] presented a new method for fast and accurate disaster loss segmentation, which integrates multi-resolution, multi-sensor, and multi-temporal satellite imagery within a CNN framework. [PERSON] [16] proposed a deep learning-based Multi-View Stereo (MVS) model for reconstructing 3D models of earthquake-damaged buildings, aimed at assisting in building damage assessment tasks. [PERSON] [17] presented EBDC-Net (Earthquake Building Damage Classification Network). The network comprises a feature extraction module and a damage classification module and was designed to augment semantic information and differentiate various damage levels. [PERSON] [18] introduced a new framework that accelerates building detection in ultra-high-resolution images. This approach employs the maximum correlation minimum redundancy method for feature selection, resulting in the generation of five distinct feature sets. While previous studies on building damage classification have yielded valuable insights, numerous challenges still need resolution. Firstly, the issue of data sample category imbalance poses a significant problem. In most seismic hazards, significantly fewer buildings are damaged or collapsed by earthquakes than buildings that are undamaged or slightly damaged.