Multispectral remote sensing images (MSIs) provide a substantial amount of ground object information spread over various spectral bands of the image. The quantity of information contained in different bands or different spatial locations within the same band varies significantly. How to capture useful information from MSIs is a challenging task in semantic segmentation of remote sensing images. An end-to-end semantic segmentation network (BLASeNet) based on band-location adaptive selection is proposed here. The proposed network adopts an encoder-decoder structure. In the coding phase, a band-location adaptive selection mechanism is proposed to adaptively learn the weights of different bands and different spatial locations within the same band, enhancing the effective features expression. The spectral-spatial features of 3D residual block-coded images are further proposed to make use of the band correlation of MSIs. During the decoding phase, an adaptive feature fusion module is proposed to adaptively adjust the fusion ratio of low-level detail features and high-level semantic features via network learning, as well as investigate the impact of three fusion strategies, namely, addition (BLASeNet-A), element multiplication (BLASeNet-M), and concatenation (BLASeNet-C), on the model's performance gain. Furthermore, channel attention is extended to 3D data, and the fused feature map is recalibrated on the channel dimension to produce a more accurate multi-level interactive feature map. The effectiveness of BLASeNet has been demonstrated by experimental results on ISPRS Potsdam, Qinghai and Tibet Plateau datasets.