It is a fact that natural disasters often cause severe damage both to ecosystems and humans. Moreover, man-made disasters can have enormous moral and economic consequences for people. A typical example is the large deadly and catastrophic explosion in Beirut on 4 August 2020, which destroyed a verylarge area of the city. This research paper introduces a Geo-AI disaster response computer vision system, capable to map an area using material from Synthetic Aperture Radar (SAR). SAR is a unique form of radar that can penetrate the clouds and collect data day and night under any weather conditions. Specifically, the Memory-Augmented Deep Convolutional Echo State Network (MA/DCESN) is introduced for the first time in the literature, as an advanced Machine Vision (MAV) architecture. It uses a meta-learning technique, which is based on a memory-augmented approach. The target is the employment of Deep Reservoir Computing (DRC) for domain adaptation. The developed Deep Convolutional Echo State Network (DCESN) combines a classic Convolutional Neural Network (CNN), with a Deep Echo State Network (DESN), and analog neurons with sparse random connections. Its training is performed following the Recursive Least Square (RLS) method. In addition, the integration of external memory allows the storage of useful data from past processes, while facilitating the rapid integration of new information, without the need for retraining. The proposed DCESN implements a set of original modifications regarding training setting, memory retrieval mechanisms, addressing techniques, and ways of assigning attention weights to memory vectors. As it is experimentally shown, the whole approach produces remarkable stability, high generalization efficiency and significant classification accuracy, significantly extending the state-of-the-art Machine Vision methods.

中文翻译：

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Geo-AI 通过内存增强的深水储层计算来帮助灾难响应

自然灾害常常对生态系统和人类造成严重破坏，这是事实。此外，人为灾难会对人们产生巨大的道德和经济后果。一个典型的例子是 2020 年 8 月 4 日在贝鲁特发生的致命和灾难性的大爆炸，摧毁了这座城市的大片区域。本研究论文介绍了一种 Geo-AI 灾难响应计算机视觉系统，能够使用合成孔径雷达 (SAR) 的材料绘制区域地图。SAR 是一种独特的雷达形式，可以在任何天气条件下日夜穿透云层并收集数据。具体来说，记忆增强深度卷积回波状态网络 (MA/DCESN) 在文献中首次被引入，作为一种先进的机器视觉 (MAV) 架构。它使用元学习技术，它基于内存增强方法。目标是使用深度水库计算 (DRC) 进行域适应。开发的深度卷积回声状态网络 (DCESN) 结合了经典的卷积神经网络 (CNN)、深度回声状态网络 (DESN) 和具有稀疏随机连接的模拟神经元。它的训练是按照递归最小二乘 (RLS) 方法进行的。此外，外部存储器的集成允许存储来自过去过程的有用数据，同时促进新信息的快速集成，无需再训练。提议的 DCESN 实现了一组关于训练设置、记忆检索机制、寻址技术以及将注意力权重分配给记忆向量的方法的原始修改。正如实验表明的那样，