###### Abstract  
Flooding in urban areas is counted as a significant disaster that must be correctly mitigated due to the huge amount of affected people, material losses, hampered economic activity, and flooded-related diseases. One of the technologies available for disaster mitigation and prevention is satellites providing image data on previously flooded areas. In most cases, floods occur in conjunction with heavy rain. Thus, from a satellite's optical sensor, the flood area is mostly covered with clouds which indicates ineffective observation. One solution to this problem is to use Synthetic Aperture Radar (SAR) sensors by observing backscatter differences before and after flood events. This research proposes mapping the flood-prone areas using machine learning to classify the areas using the 3D CNN method. The method was applied on a combination of co-polarized and cross-polarized SAR multi-temporal image datasets covering Jakarta City and the coastal area of Bekasi Regency. Testing with multiple combinations of training/testing data proportion split and a different number of epochs gave the optimum performance at an 80/20 split with 150 epochs achieving an overall accuracy of 0.71 after training in 283 min. urban flood; Sentinel-1a; Synthetic Aperture Radar (SAR); 3D Convolutional Neural Network; multi-temporal data +
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]. When a disaster occurs, urban floods usually coincide with rain, so when observed using optical sensors on remote sensing satellites, the area is covered with clouds. With this occlusion, satellite optical sensors are not effectively used for flood observation. One solution to observing floods in cloud-covered areas is to use Synthetic Aperture Radar (SAR) sensors such as Sentinel-1, ALOS PALSAR, TerraSAR-X, and other radar sensors. The image produced by SAR is a monochromatic image containing reflectance information from the observation area by observing the difference in the backscatter before and after the incident to identify the flooded area [6]. Wide-scale Earth monitoring satellites begin with the Landsat program (Land Satellite) to monitor the Earth's surface. At present, many optical satellite systems operate at high resolution. The most widely used images are from QuickBird, SPOT-5, and Worldview Series. Despite having the ability to detect very sharp objects, optical satellite systems have the potential to be unable to detect objects on the Earth's surface due to cloud cover. Until recently, SAR data processing was mostly used for rural areas rather than in urban areas. For urban areas, SAR data has its problems, namely speckle noise, because, in urban areas, many buildings cause radar waves to experience much scattering, and the reading of the reflected waves is disturbed by multipath interference [7; 8]. The double-bounce characteristic of radar signals caused by buildings is a challenge related to its contribution to SAR image speckle noise. However, it can be used to detect the presence of buildings and distinguish them from other surfaces such as soil, vegetation, and water [9; 10; 11]. The solution to detect the presence of floods through SAR images is multi-temporal filtering, which is filtering based on changes in the backscattering characteristics of SAR images taken at different times. The method used to identify and distinguish the occurrence of flood from SAR data is generally divided into two groups, namely polarization and interference. The polarization method detects the presence of water, based on changes in backscatter polarization caused by specular reflections from the water surface [12; 10; 13; 14], while the interference method detects water based on changes of coherence due to changes in the spatial distribution pattern of the object that produces the backscatter [15; 10; 16; 17]. Several studies related to floods in settlements used SAR and Light Detection and Ranging (LiDAR) data for small towns located on one side of a river and areas with homogeneous slopes [18; 19; 20].