The detection of changes in asbestos roofing is vital due to the significant health risks associated with asbestos exposure. Utilising remote sensing for this task is promising, yet it encounters challenges like misregistration and variations in illumination within temporal images. This study presents a novel object-based deep learning framework with a post-classification structure designed specifically to detect changes in asbestos roofing. The method employs a hybrid deep learning model that utilises DenseNet121 to extract spatial features and integrates with either LSTM (DenseNet-LSTM) or conv1D (DenseNet-Conv1D) to capture temporal features. Using VHR aerial imagery, image patch sequences were generated for each roofing object across seven temporal points. After training the model, labels were assigned to each image patch in the sequence through binary classification. In post-processing, sequence alignment based on Hamming distance was used to enhance label assignments by aligning them with a pre-determined set of valid label sequences. Finally, the temporal change map was generated by comparing label transitions across temporal points. The results highlighted the model's robust capability to address inconsistencies across temporal points without relying on preprocessing techniques to manage such temporal discrepancies. The DenseNet-LSTM and DenseNet-Conv1D models demonstrated comparable classification performance in asbestos roofing detection, achieving average overall accuracies of 95.8% and 96.0%, respectively. Furthermore, the model's generalisability was underscored by a test on an independent dataset, which produced up to 94% detection accuracy for asbestos class. While the framework has limitation in detecting painted asbestos roofing and partial changes, it offers a reproducible approach for change detection tasks that target a specific roofing type, rather than detecting changes in all roofing types within remote sensing imagery.