Most of the work done for landcover analysis using satellite imagery employed single-date images, which are incapable of capturing seasonal variations in landcover classes. To address this, we propose Attention-ASPP-FCN - a 3D Fully Convolutional Network (FCN) segmentation architecture based on 3D Atrous Spatial Pyramid Pooling (ASPP) modules and self-attention, to produce landcover maps that process Satellite Image Time-Series (SITS) rather than single-date satellite images, resulting in the generation of better landcover segmentation maps as the model captures the temporal dynamics of landcover, including phenological variations, seasonal changes in vegetation, and land use patterns over time. To capture long-range dependencies among features, we used two main strategies: (i) expanding the receptive field through ASPP modules that enable resampling the input feature vector at multiple dilation rates to capture the context at different scales, making the network more robust to translational variances, and (ii) incorporating a lightweight 3D timeframe-wise self-attention to assign more weightage to important timeframes in a SITS. The proposed model offers a reliable and efficient method for precisely segmenting different landcover classes from a SITS by utilizing pertinent spectral, spatial, and temporal data, achieving the highest mean Intersection over Union (mIoU) of 93.26% and 94.13%, respectively, in the Indian and Slovenian Regions of Interest (RoI). The proposed model improved the mIoU by a margin of 6.54–6.68% than that achieved by the best single-date model, thereby demonstrating the superiority of employing a SITS over single-date images for landcover segmentation.