MOSAIK

Timestamp:

May 5, 2021 11:40:00 AM (4 years ago)

Author:

hettrich

Comment:

--

project/project/subproj/wps52

@@ -7 +7 @@
 === Project Tasks:
 ||
-The work in MOSAIK Phase 1 showed the importance of automated generation of surface input parameters, as well as the provision of open-source data. As not all municipalities have the same data available at the same level of detail, it is useful to have a base layer available that consistently provides a minimum amount of spatial input parameters and which can be supplementary, if necessary, with local parameters. This basic data-set should only consist of open-source data, if possible, to allow their use for everyone, who intends to test PALM-4U. Many open-source data not only exist for Germany, but also for Europe and globally. Therefore, it would allow the transferability of PALM-4U to cities outside of Germany. 
 
-Additionally to the need for a basic data-set, the demand for further spatial input parameters, which will be developed in this work package, has been identified during the development of PALM in MOSAIK-1. Therefore, the goal of work package WP-S5.2 is to improve the surface input parameters for PALM-4U in two different ways: 
+'''I) Development of analysis-ready surface data sets'''
+|| 
+The work in MOSAIK Phase 1 showed the importance of an automated generation of surface input parameters, as well as the provisioning of analysis-ready data. As not all municipalities have the same data available at the same level of detail, it was deemed important to provide base-layers that consistently provide a minimum amount of spatial input parameters, which can be supplemented, if necessary, with local parameters. This basic data-set should only consist of freely accessible data, if possible, to allow their use for everyone intending to test PALM-4U. Many open-source data not only exist for Germany, but also for Europe and globally. Therefore, such base-layers allow the transferability of PALM-4U to cities outside of Germany. 
+In addition to deriving and providing the data layers themselves, work is performed to create and document stand-alone data retrieval software, which could then be distributed with PALM-4U allowing input data retrieval and preparation also on the user side.
+||
+'''II) Development of methods to integrate new high-resolution input data.'''
+||
+In addition to the need for a consistent base-layer data-set, the demand for further spatial input parameters has been identified during the development of PALM in MOSAIK-1. Therefore, the second goal of this work package is to explore new methods of deriving information not readily available in other published data-sets. In particular, we are looking to improve the description of building characteristics from aerial imagery using novel machine learning approaches.
 
-1) Development of an open-source surface data set(s) from which the input parameters can be derived automatically. 
+||
+=== Project structure
+||
+This project is conducted at the German Aerospace Centre in Oberpfaffenhofen.
 
-2) Development of methods to integrate new high-resolution input data.
+PI: Julian Zeidler
+ 
+Projektmitarbeiter: Dr. Benjamin Leutner
+||
+=== Progress so far:
+||
+'''Base-layer data retrieval and preparation '''
 
-
+Data sets have been curated from open-access data-sources and distributed to the project partners. This includes layers such as vegetation heights, surface materials or elevation (Figure 1).
 
 {{{
@@ -25 +40 @@
 '''Figure 1:''' Examples of input parameter (parts of) the study area of Berlin, Germany, for PALM-4U. Created by DLR. Data sources: FIS Broker Berlin (LiDAR, CIR imagery, CityGML),  Open Street Map (OSM) and SRTM
 
-||
-=== Project structure
-||
-This project is conducted at the German Aerospace Centre in Oberpfaffenhofen.
+'''AI based building information extraction from aerial imagery.'''
 
-PI: Dr. Wieke Heldens
-||
-=== Deliverables:
-||
-||
-=== Progress so far:
-||
+Recent developments in the realm of artificial intelligence allow for increasingly detailed information retrieval from very high-resolution imagery. In this task we explore the potential of oblique aerial imagery to retrieve detailed building façade information. 
+To this end, we implemented and trained a deep-learning based classifier, which performs instance segmentation on two-dimensional images. In particular it maps facade window area, an important component of building heat exchange, based on texture images from a 3D city model of Berlin (LOD2 CityGML).  Training data, sourced from publicly available data-sets, were used in a first iteration to train a Mask-RCNN model. In a second, transfer-learning step the model was fine-tuned to manually labelled 2d images extracted from the LOD2 data. This final model was the validated and used to annotate all unlabeled textures from the LOD2 data, hence creating a map of façade window fractions.
+
+{{{
+#!div style="align:center; width: 800px; border: 0px solid"
+[[Image(WP-S52-figure2.png,nolink,800px,center)]]
+}}}
+
+
+'''Figure 2:''' Model-based extraction of façade window area from oblique aerial imagery (provided in CityGML LOD2 format) by means of an instance segmentation algorithm.
+
 ||
 === References
@@ -48 +64 @@
 === Contact:
 ||
-Dr. Wieke Heldens
+Julian Zeidler
 
-Wieke.Heldens[at]dlr.de
+Julian.Zeidler[at]dlr.de 
 
 Deutsches Zentrum für Luft u. Raumfahrt e.V. (DLR)