Pre-disaster information storage is crucial for effective disaster response. The discussion regarding deep learning-based Light Detection and Ranging (Lidar) semantic segmentation technology for indoor small items has been ongoing in recent years. However, the methods applicable to large-scale outdoor Lidar datasets for pre-disaster information storage remain limited. This study aims to propose a novel deep learning-based network for city-scale Lidar semantic segmentation to support pre-disaster information storage, called channel attention and normal-based local feature aggregation network (CNLNet). This network is designed to segment common urban land cover objects, including buildings and vegetation. This network incorporates surface normal information and the channel attention mechanism into the RandLA-Net backbone. Ablation studies have been devised to assess the performance of these two features. During the pre-processing step, color information from optical images is fused with Lidar data. The findings demonstrate that CNLNet can enhance the accuracy of the RandLA-Net backbone by improving mIoU at least 1-2%. Including one of these two features also contributes to the backbone’s improved accuracy. Notably, CNLNet outperforms other well-known networks in terms of accuracy with the test of the public Sementic3D dataset. The study further reveals that the proposed network excels in building segmentation, a crucial facet of pre-disaster information storage. Moreover, the results show that spatial resolution, whether at 0.5m or 10m per pixel for optical images, has limited influence on outcomes. One theoretical contribution of this study is the demonstration of the advantages of integrating either surface normal information or a channel attention mechanism to enhance large-scale outdoor Lidar semantic segmentation. Labeled Lidar datasets have been created for training. The practical contribution is that it can optimize disaster response by efficiently facilitating pre-disaster information storage.

Wildfires have attracted considerable attention because of their increasing frequency and severity around the globe. Satellite remote sensing data is a valuable asset for monitoring, and mapping burned areas (BA). However, most global BA products based on optical imagery are limited by cloud coverage and not usable for cloud-prone regions. All-weather Synthetic Aperture Radar (SAR) imagery can be a complement to an optical-based counterpart. In order to exploit the value of phase information of SAR data, this paper aims to propose a framework by developing a visual saliency detection algorithm for BA mapping using Sentinel-1 Interferometric SAR (InSAR) coherence difference image. The results show that the proposed method can effectively improve the coherence difference's accuracy performance. Additionally, we also demonstrate that for C-band Sentinel-1 SAR data, both VV and VH polarized images can be used in BA mapping, but the former would provide slightly better results.

The ancient Aniangzhai (ANZ) landslide in Danba County, Sichuan Province of southwest China was reactivated after a series of complex hazard events that occurred in June 2020. Since then, emergency engineering work was carried out to prevent further failure of the reactivated landslide. This study investigates the multi-temporal optical images (3 m spatial resolution) acquired from the PlanetScope satellite with pixel offset tracking (POT) technique to assess deformation characteristic and spatial-temporal evolution of the reactivated ANZ landslide during the post-failure stage. The relationships between sun illumination differences, temporal baseline of correlation pairs and the uncertainties were explored. The large horizontal displacements over the reactivate ANZ slope were detected from the time-series POT results, showing a significant increase of about 24 m between 24 June 2020 and 11 June 2021. The time series optical POT results revealed that the reactivated ANZ landslide body is gradually slowing down to a steady deformation status since its occurrence in August 2020, indicating the effectiveness of engineering work on the prevention of further landslide.

Most deep learning networks for Lidar semantic segmentation have been devoted to small-scale indoor data and only few of them have focused on large-scale outdoor data. To bridge this gap, this research explores the influences of changing features of deep learning networks on the accuracy of large-scale outdoor Lidar semantic segmentation. Surface normal information and random downsampling layers are the two features considered. Eight scenarios are designed to test them. Point clouds acquired from Kapiti Coast, New Zealand in 2021 with five labeled classes are used for training, validation, and testing stages. Mean intersection over union (mIOU) is the main metric in the validation and test. The findings show that the network adding surface normals with four random downsampling layers whose sampling ratios are 4, 4, 4, and 4 of those layers performs best because of its high mIOU. Moreover, IOU results reflect that the segmentation of buildings performs best between all tested classes.

In 2022, flooding severely violated Australia, resulting in the displacement of residents and damage to property and public facilities. With the rapid development of information technology, it is possible to use Synthetic Aperture Radar (SAR) satellite remote sensing technology and the Quadrotor Unmanned Aerial Vehicle (QUAV) to detect and assess flooding environments. SAR can penetrate the cloud to operate at all times and in all weather, which is ideal for flooding area mapping. However, most SAR-based products are constrained by the flood’s dynamically shifting boundary and spatial and temporal resolution. QUAV is portable and capable of precise positioning despite being ineffective in covering large areas, such as flood-affected areas. Thus, it can complement the SAR counterpart for flood mapping in boundary extraction. This paper aims to propose a framework that mainly fuses satellite SAR and QUAV technology by aggregating the multiple-scale data for double validation and detailing, with enhancement by deep learning-based prediction models and a closed-loop feedback mechanism, to form a novel space-air vertical remote sensing framework. Finally, the selected flood-affected areas in Moama, NSW, Australia, were conducted as a case study. The results show that the proposed method can effectively enhance flood area assessment and mapping.

Early detection of bushfire plays a crucial role in firefighting, fire modelling, and minimising losses of human lives and properties. However, current bushfire monitoring systems have an intrinsic shortcoming because only temperature difference between neighboring pixels is exploited. This paper proposes to also examine a range of other changes occur when a bushfire is ignited, for example, a reduction of vegetation cover, volume scattering of bush and trees, as well as height of vegetation. All of these can be readily measured by optical and radar satellites already in orbits in near real-time, that is, less than two hours after a satellite overpass. Cross-correlation of these measurements has the potential to significantly reduce false alarm of a bushfire, while improving the early detection and measurement of fire spots, and hence make the system much more robust. A case study near Sydney is included here based on Sentinel-1 SAR and Sentinel-2 optical satellite data collected on 10 and 11 October 2020, respectively. This research is a major step forward towards the operational and synergetic use of optical and SAR satellites in bushfire monitoring.

Building damage assessment after natural disasters is an important task for disaster managers and practitioners. In order to provide detailed levels of post-event building damage, this paper applies deep learning models for building localization and damage classification using transfer learning with an online free xBD dataset. The model is pretrained with ImageNet dataset. SE-ResNeXt-50-32x4d is applied for building localization, and HRNet is applied for damage classification. The building damage is divided into four levels, including no damage, minor damage, major damage, and total damage. The results show that the method can be applied for classifying building damage in an acceptable manner. This can help the government and rescue teams make disaster response quickly and support disaster management.