Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Earth observation (EO)-based mapping and analysis of natural hazards plays a critical role in various aspects of post-disaster aid management. Spatial very high-resolution Earth observation data provide important information for managing post-tsunami activities on devastated land and monitoring re-cultivation and reconstruction. The automatic and fast use of high-resolution EO data for rapid mapping is, hover, complicated by high spectral variability in densely populated urban areas and unpredictable textural and spectral land-surface changes. The present paper presents the results of the SENDAI project, which developed an automatic post-tsunami flood-extent modelling concept using RapidEye multispectral satellite data and ASTER Global Digital Elevation Model Version 2 (GDEM V2) data of the eastern coast of Japan (captured after the Tohoku earthquake). , the authors developed both a bathtub-modelling approach and a cost-distance approach, and integrated the roughness parameters of different land-use types to increase the accuracy of flood-extent modelling. Overall, the accuracy of the developed models reached 87–92%, depending on the analysed test site. The flood-modelling approach was explained and results re compared with published approaches.  came to the conclusion that the cost-factor-based approach reaches accuracy comparable to published results from hydrological modelling. Hover the proposed cost-factor approach is based on a much simpler dataset, which is available globally.
['Sören Hese', 'Thomas Heyer']
I am writing a research paper on a idea Earth observation data based rapid flood-extent modelling for tsunami-devastated coastal areas
[{"from": "human", "value": "I am writing a research paper on a idea Earth observation data based rapid flood-extent modelling for tsunami-devastated coastal areas"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Earth observation (EO)-based mapping and analysis of natural hazards plays a critical role in various aspects of post-disaster aid management. Spatial very high-resolution Earth observation data provide important information for managing post-tsunami activities on devastated land and monitoring re-cultivation and reconstruction. The automatic and fast use of high-resolution EO data for rapid mapping is, hover, complicated by high spectral variability in densely populated urban areas and unpredictable textural and spectral land-surface changes. The present paper presents the results of the SENDAI project, which developed an automatic post-tsunami flood-extent modelling concept using RapidEye multispectral satellite data and ASTER Global Digital Elevation Model Version 2 (GDEM V2) data of the eastern coast of Japan (captured after the Tohoku earthquake). , the authors developed both a bathtub-modelling approach and a cost-distance approach, and integrated the roughness parameters of different land-use types to increase the accuracy of flood-extent modelling. Overall, the accuracy of the developed models reached 87\u201392%, depending on the analysed test site. The flood-modelling approach was explained and results re compared with published approaches.  came to the conclusion that the cost-factor-based approach reaches accuracy comparable to published results from hydrological modelling. Hover the proposed cost-factor approach is based on a much simpler dataset, which is available globally."}]