Bangladesh is highly susceptible to land use land cover (LULC) changes due to its geographical location. These changes have significant effects on food security, urban development, and natural resource management. Policy planning and resource management largely depend on accurate and detailed LULC maps. However, Bangladesh does not have its own national scale detailed high-resolution LULC maps. This study aims to develop high-resolution land use land cover (HRLULC) maps for Bangladesh for the years 2020 and 2023 using a deep learning method based on convolutional neural network (CNN), and to analyze LULC changes between these years. We used an advanced LULC classification algorithm, namely SACLASS2, that was developed by JAXA to work on multi-temporal satellite data from different sensors. Our HRLULC maps with 14 categories achieved an overall accuracy of 94.55% ± 0.41% with Kappa coefficient 0.93 for 2020 and 94.32% ± 0.42% with Kappa coefficient 0.93 for 2023, which is higher than the commonly accepted standard of around 87% overall accuracy for 14 category LULC map. Between 2020 and 2023, the most notable LULC increase were observed in single cropland (17% ± 4%), aquaculture (20% ± 5%), and brickfield (56% ± 25%). Conversely, decrease occurred for salt pans (47% ± 16%), bare land (24% ± 3%), and built-up (13% ± 3%). These findings offer valuable insights into the spatio-temporal patterns of LULC in Bangladesh, which can support policymakers in making informed decisions and developing effective conservation strategies aimed at promoting sustainable land management and urban planning.