The intertidal zones are well recognized for their dynamic nature and role in near-shore hydrodynamics. The intertidal topography is poorly mapped worldwide due to the high cost of associated field campaigns. Here we present a combination of remote-sensing and hydrodynamic modeling to overcome the lack of in situ measurements. We derive a digital elevation model (DEM) by linking the corresponding water level to a sample of shorelines at various stages of the tide. Our shoreline detection method is fully automatic and capable of processing high-resolution imagery from state-of-the-art satellite missions, e.g., Sentinel-2. We demonstrate the use of a tidal model to infer the corresponding water level in each shoreline pixel at the sampled timestamp. As a test case, this methodology is applied to the vast coastal region of the Bengal delta and an intertidal DEM at 10 m resolution covering an area of 1134 km 2 is developed from Sentinel-2 imagery. We assessed the quality of the DEM with two independent in situ datasets and conclude that the accuracy of our DEM amounts to about 1.5 m, which is commensurate with the typical error bar of the validation datasets. This DEM can be useful for high-resolution hydrodynamic and wave modeling of the near-shore area. Additionally, being automatic and numerically effective, our methodology is compliant with near-real-time monitoring constraints.
While strides have been made in deep learning based Bengali Optical Character Recognition (OCR) in the past decade, absence of large Document Layout Analysis (DLA) datasets has hindered the application of OCR in document transcription, e.g., transcribing historical documents and newspapers. Moreover, rule-based DLA systems that are currently being employed in practice are not robust to domain variations and out-of-distribution layouts. To this end, we present the first multidomain large Bengali Document Layout Analysis Dataset: BaDLAD. This dataset contains 33, 695 human annotated document samples from six domains -i) books and magazines ii) public domain govt. documents iii) liberation war documents iv) new newspapers v) historical newspapers and vi) property deeds; with 710K polygon annotations for four unit types: text-box, paragraph, image, and table. Through preliminary experiments benchmarking the performance of existing state-of-the-art deep learning architectures for English DLA, we demonstrate the efficacy of our dataset in training deep learning based Bengali document digitization models.
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