Accurate and fine-grained information about the extent of damage to buildings
is essential for directing Humanitarian Aid and Disaster Response (HADR)
operations in the immediate aftermath of any natural calamity. In recent years,
satellite and UAV (drone) imagery has been used for this purpose, sometimes
aided by computer vision algorithms. Existing Computer Vision approaches for
building damage assessment typically rely on a two stage approach, consisting
of building detection using an object detection model, followed by damage
assessment through classification of the detected building tiles. These
multi-stage methods are not end-to-end trainable, and suffer from poor overall
results. We propose RescueNet, a unified model that can simultaneously segment
buildings and assess the damage levels to individual buildings and can be
trained end-toend. In order to to model the composite nature of this problem,
we propose a novel localization aware loss function, which consists of a Binary
Cross Entropy loss for building segmentation, and a foreground only selective
Categorical Cross-Entropy loss for damage classification, and show significant
improvement over the widely used Cross-Entropy loss. RescueNet is tested on the
large scale and diverse xBD dataset and achieves significantly better building
segmentation and damage classification performance than previous methods and
achieves generalization across varied geographical regions and disaster types.

RescueNet 是一个集成模型，采用了局部相关的二进制交叉熵损失函数来同时分割建筑和评估破坏水平，该模型可以进行端到端的训练，并获得比现有方法更好的性能，它可以在各种地理区域和灾难类型下实现通用。