From: ~Tim_Georg_Johann_Rudner1
comment: We would like to thank the reviewer for their thoughtful and insightful comments.

1) Segmentation method

We would like to first address the reviewer’s question about the connection between segmenting buildings and providing information useful to first responders and authorities.

The primary goal of this work is to identify *flooded buildings*. To achieve this goal, we treat building footprint segmentation as a proxy objective. Doing so allows us to (1) pretrain our network on more broadly available building footprint data and (2) compare our approach with other segmentation methods on datasets such as the INRIA Aerial Labels benchmark dataset. We will state this connection more clearly in the final draft of the paper.

By pretraining the network on building footprints, the network learns to extract features relevant for building identification. Modifying the model objective to perform segmentation of flooded buildings then effectively encourages the model to extract features of buildings surrounded by water. 

The goal of this work is to help first responders decide which neighborhoods to target first in order to provide help to those most in need. We worked with the European Space Agency, disaster relief organizations, and NGOs to ensure that our contribution is meaningful and has impact. We were advised to focus on the segmentation of flooded buildings to help first responder assess, not necessarily which areas might be most flooded, but in which areas most people were affected.


2) Data and training details

Table 1 is based on the Houston dataset described in the “Data” subsection of the paper and presents the performance of our model for different inputs. To create the dataset, we gathered two sets of open-source ground truth labels: building footprint polygons obtained from OpenStreetMap (OSM) and point annotations of flooded buildings from the DigitalGlobe Open Data Program. We spatially joined the OSM building polygons and point annotations by subsetting for polygons that contain flood annotations and rasterized them into two binary label maps: one for all labeled OSM buildings and one for buildings labeled as flooded. While the completeness and coordinate accuracy of OSM labels vary by region, we were able to identify several neighborhoods in Houston with high label accuracy.

The INRIA dataset contains pairs of very high resolution (VHR) satellite imagery and rasterized building footprints from OSM for multiple cities worldwide which is conceptually similar to our VHR-only approach.

To avoid spatial autocorrelation, we divided the area of interest into two spatially distinct partitions (i.e. non-overlapping subsets) covering two different neighborhoods. For a map of the area of interest that shows the partitions more clearly, please see https://goo.gl/JSzHfJ. We randomly divided the East partition into training and a validation sets at a 4:1 split. The hyperparameters were optimized on the validation set. All model evaluations presented in this work were performed on the spatially separate test dataset. The East partition (train/validation) contained 4,500 labeled buildings and 2,300 flooded building annotations and the West partition (test) 2,600 labelled buildings and 1,500 flooded building annotations.


3) Generalization

We acknowledge the reviewer’s concern about our method’s ability to generalize. Although, for this work, we were limited to publicly available data and relied on label data crowdsourced through the DigitalGlobe-Crowdsourcing Initiative, we did perform additional experiments for different disaster types to assess our methods ability to generalize. In particular, we used our method to perform segmentation of collapsed buildings in the Ecuadorian town of Portoviejo following an earthquake in 2016. For this task, we initialized the model with the weights obtained from our building footprint segmentation model, reducing training time. Figure (2b) in https://goo.gl/vyw3tt shows that our model was able to correctly identify collapsed buildings (A and B) as well as buildings labeled as severely damaged (C and D), providing a first proof of concept that our method is able to generalize across damage types.


We thank the reviewer for their suggestions and comments. We hope that we have clarified any open questions.
title: Clarification about application, data, and model generalization

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper27/AnonReviewer2
evaluation: This is an interesting work in which satellite imagery from multiple sensors at multiple resolutions are fused and used as input to CNNs to generate building segmentation maps with the aim of aiding responders and local authorities in the event of floods. The application area of disaster response is important, and the paper is enlightening in showing segmentation performance using publicly-available medium-resolution data vs. expensive high resolution data vs. fusing the two together. It was also interesting to learn that late fusion works better than early fusion in this setting.

Some questions, comments, and suggestions:

The paper doesn't provide a strong connection between segmenting buildings and providing information that is useful to responders and authorities. Is it the change in building footprint that is useful? Or is it actually the footprint of water more generally, not only in buildings but also other public areas like on streets, piers, etc.? This was not made clear in the introduction.

What was the ground truth dataset that provided building footprint segmentation for training? The INRIA dataset is mentioned briefly in the caption of Table 2 but was not elaborated on. I also don't recall an explanation of the Table 1 dataset. How many labels were available? How were labels acquired? How were the training and test set labels split? Is spatial autocorrelation an issue in this dataset? These are all very important as they will determine the generalizability of such a model.

Since the motivation for the work is timely flood maps for disaster response, how do the models perform in a simulated realistic setting? That is, instead of training on labels and imagery from Hurricane Harvey and testing on labels and imagery from the same, how well does a model trained on various past flood events generalize to a new flood event?

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper27/AnonReviewer2
relevance: 4: high
impact: 4: high
novelty: 3: borderline