In recent hyperspectral unmixing (HU) literature, the application of deep learning (DL) has become more prominent, especially with the autoencoder (AE) architecture. We propose a split architecture and use a pseudo-ground truth for abundances to guide the `unmixing network' (UN) optimization. Preceding the UN, an `approximation network' (AN) is proposed, which will improve the association between the centre pixel and its neighbourhood. Hence, it will accentuate spatial correlation in the abundances as its output is the input to the UN and the reference for the `mixing network' (MN). In the Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness (GAUSS), we proposed using one-hot encoded abundances as the pseudo-ground truth to guide the UN; computed using the k-means algorithm to exclude the use of prior HU methods. Furthermore, we release the single-layer constraint on MN by introducing the UN generated abundances in contrast to the standard AE for HU. Secondly, we experimented with two modifications on the pre-trained network using the GAUSS method. In GAUSS$_\textit{blind}$, we have concatenated the UN and the MN to back-propagate the reconstruction error gradients to the encoder. Then, in the GAUSS$_\textit{prime}$, abundance results of a signal processing (SP) method with reliable abundance results were used as the pseudo-ground truth with the GAUSS architecture. According to quantitative and graphical results for four experimental datasets, the three architectures either transcended or equated the performance of existing HU algorithms from both DL and SP domains.

应急响应高度依赖事件报告的时间。不幸的是，接收事故报告的传统方法（例如，在美国拨打911）的时间延迟。 Waze等众包平台为早期识别事故提供了机会。然而，由于与此类数据相关的噪声和不确定性的挑战，从众包数据流中的检测事件是困难的。此外，简单地通过检测精度优化可以损害推断的空间 - 时间定位，从而使得现实世界部署的这种方法不可行。本文介绍了采用众群数据作为征集数据作为征用数据的新的问题配方和解决方案方法，以应急响应管理作为案例研究。所提出的方法CROME（众包多目标事件检测）量化了事件分类（例如，F1得分）的性能度量与模型从业者的要求之间的关系（例如，1公里。入射检测的半径）。首先，我们展示了众包报告，地面真实历史数据和其他相关决定因素，如交通和天气，可以在卷积神经网络（CNN）架构中一起使用，以便早期检测紧急事故。然后，我们使用Pareto优化的方法来优化CNN的输出，以便以从业者为中心的参数来平衡检测精度和空间 - 时间本地化。最后，我们展示了这种方法使用来自美国纳什维尔的Waze和交通事故报告的众群数据的适用性。我们的实验表明，所提出的方法优于事件检测的现有方法，同时优化现实世界部署和可用性的需求。