Coastal and estuarine ecosystems are facing spatiotemporal changes and suffer from the effects of accelerated natural destructive processes due to climate change. Monitoring these areas is crucial to protect them and maintain the ecological balance of shorelines. In this context, full-waveform airborne topo-bathymetric lidar is a reliable tool to collect data seamlessly over land-water continuum zones, thanks to its dual wavelength configuration. It is therefore optimal for coastal habitats monitoring and mapping. However, lidar waveform processing often relies on peak detection and feature extraction that are difficult to conFigure and often sensitive to noise. In this article, we rather suggest not to rely on hand-crafted features by relying on U-time, a neural network inspired by the well-known UNet convolutional neural network, to identify peaks in waveforms and classify them to discriminate coastal ecosystems efficiently. The network is tested on green waveforms and we evaluate in addition the contribution of infrared intensities. Results show equivalent performances, and obtain over 92% of accuracy when accepting a 2 samples margin of error for peaks location, which does not impact heavily waveform analysis, considering usual peaks widths. Our study shows green waveforms alone allow habitats detection with a F-score of 94%, outperforming previous methods.