Comparing the effectiveness of Landsat-derived spectral indices for building age prediction in urban energy modelling

The building stock accounts for 34% of global energy demand and 37% of CO₂ emissions related to energy and industrial processes. Additionally, the current increase in urbanization rates poses significant environmental challenges. Policy makers are becoming increasingly aware of these impacts, developing strategies aimed at improving energy efficiency and obtaining decarbonization of the built environment. Achieving these goals requires modeling actual building stock energy consumption patterns, future energy developing trends as well as the impact of energy retrofitting measures on CO₂ emissions. Urban Building Energy Models (UBEMs) and bottom-up engineering models have proven to be valuable tools. However, these models  require detailed and accurate building attributes related to physical properties (building geometry, height, building type, thermal transmittances, etc.), local climate (air temperature, humidity, solar radiation, etc.) and data related to occupants' energy behavior (occupants’ schedule, heating and cooling energy demand, efficiency of the system etc.). Among others, building construction year is one of the most relevant parameters since it is a key proxy for essential characteristics such as morphology, facade design, building materials, and energy efficiency. However, obtaining building construction year is particularly challenging as it is rarely available in public databases and, when available, the data are often incomplete or inconsistent. In this regard, remote sensing techniques can play a crucial role in the study and monitoring of the building stock. In particular, satellite images represent an excellent tool for the estimation of building age at local or regional scale given their extensive temporal and spatial coverage, as well as and the continuous updates of collections. The study focuses on the city of Parma, for which seven images covering the year range between 1985 and 2011 were selected. After a literature review, five built-up area extraction indices suitable for TM sensor were selected: Normalized Difference Built-up Area Index (NDBI), New Built-up Index (NBI), Band Ratio for Built-up Area (BRBA), Normalized Built-up Area Index (NBAI), and Vegetation Index Built-up Index (VIBI). In addition, Normalized Difference Vegetation Index (NDVI) was also considered, leading to a total of six indices. To improve the ability of these indexes to discriminate urban surfaces from areas with similar spectral signature (bare soil, sand, rock, etc.) annual greenest pixel composite images were generated using Google Earth Engine. Indexes performance was then compared on each image evaluating Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves, as well as performance metrics such as F1-score and Area Under the Curve (AUC). The results indicate that the NDVI is the best- Finally, temporal series were derived from the classification of images from different years, enabling the assessment of urbanization growth over time and, consequently, the estimation of building ages.

This study was carried out within the RETURN Extended Partnership and received funding from the European Union Next-GenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005) – SPOKE TS 1