As one of the most destructive natural disasters, earthquakes have struck many countries around the world in recent years, causing serious economic losses. Change detection (CD) can be applied to postearthquake building CD as it can infer interested change regions from multitemporal remote sensing (RS) images. Furthermore, the CD with short imaging intervals will better satisfy the needs of the emergency rescues after earthquakes. However, the capability of current methods built on deep neural networks (DNNs) is limited because the dataset with short imaging intervals is absent. To meet postdisaster immediate relief, we create a CD dataset, the Turkey earthquake CD dataset (TUE-CD), for the detection of building collapse in the short term after an earthquake. Due to the high requirement for timeliness of postevent images, the orbit of the satellite during postevent imaging deviates from that during preevent imaging, which leads to a side-looking problem between bitemporal images. To deal with these challenges, we present a multiscale feature interaction network (MSI-Net) for efficient interaction between bitemporal features, as well as mitigating the effect of side-looking problems. Specifically, the proposed MSI-Net consists of joint cross-attention (JCA) modules, multiscale offset calibration (MOC) modules, and feature integration (FeI) modules. The JCA module unifies channel cross-attention (CCA) and spatial joint attention (SJA) for sufficient feature interaction. The MOC module further estimates the offsets to align the bitemporal image with the multiscale features. Finally, calibrated features and multiscale features are fused by FeI modules for the prediction of changed areas. The best mF1 and mIoU scores are achieved on two public datasets and the constructed TUE-CD dataset: WHU-CD (95.58%, 91.81%), CLCD (82.96%, 73.53%), and TUE-CD (78.02%, 68.48%). Experimental results demonstrate that the proposed MSI-Net provides competitive performance compared to the s...