Quick and automated earthquake-damaged building detection from post-event
satellite imagery is crucial, yet it is challenging due to the scarcity of
training data required to develop robust algorithms. This letter presents the
first dataset dedicated to detecting earthquake-damaged buildings from
post-event very high resolution (VHR) Synthetic Aperture Radar (SAR) and
optical imagery. Utilizing open satellite imagery and annotations acquired
after the 2023 Turkey-Syria earthquakes, we deliver a dataset of coregistered
building footprints and satellite image patches of both SAR and optical data,
encompassing more than four thousand buildings. The task of damaged building
detection is formulated as a binary image classification problem, that can also
be treated as an anomaly detection problem due to extreme class imbalance. We
provide baseline methods and results to serve as references for comparison.
Researchers can utilize this dataset to expedite algorithm development,
facilitating the rapid detection of damaged buildings in response to future
events. The dataset and codes together with detailed explanations are made
publicly available at
https://github.com/ya0-sun/PostEQ-SARopt-BuildingDamage.

这篇研究论文介绍了首个专门用于从事件后的高分辨率合成孔径雷达和光学成像中检测地震受损建筑的数据集，利用开放卫星图像和标注后的土耳其 - 叙利亚地震数据，提供了 4000 多个建筑物的光学和合成孔径雷达的共配登记建筑物轮廓和图像片段的数据集，将受损建筑的检测任务视为二值图像分类问题，还可视为由于极端类别不平衡而进行的异常检测问题，提供了基线方法和结果以供对比参考，研究人员可以利用该数据集加速算法开发，促进对未来事件的快速受损建筑检测。

QuickQuakeBuildings：用于快速损坏建筑物检测的地震后 SAR - 光学数据集

QuickQuakeBuildings: Post-earthquake SAR-Optical Dataset for Quick  Damaged-building Detection

A reliable yet inexpensive tool for the estimation of flood water spread is
conducive for efficient disaster management. The application of optical and SAR
imagery in tandem provides a means of extended availability and enhanced
reliability of flood mapping. We propose a methodology to merge these two types
of imagery into a common data space and demonstrate its use in the
identification of affected populations and infrastructure for the 2022 floods
in Pakistan. The merging of optical and SAR data provides us with improved
observations in cloud-prone regions; that is then used to gain additional
insights into flood mapping applications. The use of open source datasets from
WorldPop and OSM for population and roads respectively makes the exercise
globally replicable. The integration of flood maps with spatial data on
population and infrastructure facilitates informed policy design. We have shown
that within the top five flood-affected districts in Sindh province, Pakistan,
the affected population accounts for 31 %, while the length of affected roads
measures 1410.25 km out of a total of 7537.96 km.

本文提出了一种将光学和 SAR 遥感图像融合到共同数据空间中的方法，并在巴基斯坦 2022 年洪水中进行实证研究，揭示了洪水泛滥对地区人口和基础设施的影响。

利用 Sentinel-1、Sentinel-2 和 Landsat-9 图像融合改进洪水制图以识别受洪灾影响的人口和基础设施以实现高效政策设计

Improved flood mapping for efficient policy design by fusion of  Sentinel-1, Sentinel-2, and Landsat-9 imagery to identify population and  infrastructure exposed to floods

Recognising individual trees within remotely sensed imagery has important
applications in forest ecology and management. Several algorithms for tree
delineation have been suggested, mostly based on locating local maxima or
inverted basins in raster canopy height models (CHMs) derived from Light
Detection And Ranging (LiDAR) data or photographs. However, these algorithms
often lead to inaccurate estimates of forest stand characteristics due to the
limited information content of raster CHMs. Here we develop a 3D tree
delineation method which uses graph cut to delineate trees from the full 3D
LiDAR point cloud, and also makes use of any optical imagery available
(hyperspectral imagery in our case). First, conventional methods are used to
locate local maxima in the CHM and generate an initial map of trees. Second, a
graph is built from the LiDAR point cloud, fused with the hyperspectral data.
For computational efficiency, the feature space of hyperspectral imagery is
reduced using robust PCA. Third, a multi-class normalised cut is applied to the
graph, using the initial map of trees to constrain the number of clusters and
their locations. Finally, recursive normalised cut is used to subdivide, if
necessary, each of the clusters identified by the initial analysis. We call
this approach Multiclass Cut followed by Recursive Cut (MCRC). The
effectiveness of MCRC was tested using three datasets: i) NewFor, ii) a
coniferous forest in the Italian Alps, and iii) a deciduous woodland in the UK.
The performance of MCRC was usually superior to that of other delineation
methods, and was further improved by including high-resolution optical imagery.
Since MCRC delineates the entire LiDAR point cloud in 3D, it allows individual
crown characteristics to be measured. By making full use of the data available,
graph cut has the potential to considerably improve the accuracy of tree
delineation.

通过使用图割方法和充分利用可用数据，开发了一种三维树木描绘方法，可以识别森林中的个体树木并获得精确的树冠特征数据。