Canopy height is a fundamental metric for extracting valuable information about forested areas. Over the past decade, the light detection and ranging (LiDAR) technology has provided a straightforward method for measuring canopy height using various platforms, including terrestrial, uncrewed aerial vehicles (UAVs), airborne, and satellite sensors. However, despite its global reach, spaceborne LiDAR data suffers from a sparse sampling pattern that fails to provide continuous global coverage. In contrast, satellites like LANDSAT deliver seamless and extensive coverage of the Earth’s surface through spectral data. This study aims to develop a deep learning model to infer canopy heights from sparsely observed LiDAR data, utilizing the multisensor spectral data from spaceborne platforms. Specifically tailored for localized sites, the model focuses on regional-level canopy height estimation by leveraging the relationship between canopy height and multisensor time-series data from Landsat, Sentinel-2, and Sentinel-1. We first demonstrate the importance of integrating multisensor data by training three separate models: one using only Landsat data, one using only Sentinel-2 data, and a multimodal model that incorporates Landsat, Sentinel 1, and Sentinel 2 data to estimate LiDAR-derived canopy height. These models were tested on two sites in Indiana—Tippecanoe and Monroe counties—where the multimodal approach produced the best results, achieving RMSEs of 3.895 and 4.993 m, respectively. We then tested our multimodal model in two additional counties—Baker County, FL, USA and Piute County, UT, USA—where the model achieved an RMSE of 5.397 and 3.742 m, respectively.

Local climate zones (LCZs) established a standard classification system to categorize the landscape universe for improved urban climate studies. Existing LCZ mapping is guided by human interaction with geographic information systems (GIS) or modelled from remote sensing (RS) data. GIS-based methods do not scale to large areas. However, RS-based methods leverage machine learning techniques to automatize LCZ classification from RS. Yet, RS-based methods require huge amounts of manual labels for training. We propose a novel LCZ mapping framework, termed AutoLCZ, to extract the LCZ classification parameters from high-resolution RS modalities. We study the definition of numerical rules designed to mimic the LCZ definitions. Those rules model geometric and surface cover parameters from LiDAR data. Correspondingly, we enable LCZ classification from RS data in a GIS-based scheme. The proposed AutoLCZ method has potential to reduce the human labor to acquire accurate metadata. At the same time, AutoLCZ sheds light on the physical interpretability of RS-based methods. In a proof-of-concept for New York City (NYC) we leverage airborne LiDAR surveys to model four LCZ parameters to distinguish eight LCZ types. The results indicate the potential of AutoLCZ as a promising avenue for large-scale LCZ mapping from RS data.

In this paper, we address the need for extracting two key building elevation attributes—Lowest Adjacent Grade (LAG) and Highest Adjacent Grade (HAG)—which are crucial for effective flood risk management. Conventional methods, involving onsite surveying or the use of optical imagery-derived building footprints combined with Digital Elevation Models (DEMs), often face misalignment and time discrepancy issues due to varied remote sensing sources. We introduce a new, scalable method that exclusively relies on airborne LiDAR data to overcome these challenges. Our approach employs an object-based ground filtering technique, and the results were evaluated using two different DEMs and building footprint sets. The findings demonstrate that our single-source method, utilizing only airborne LiDAR data, significantly improves the accuracy of LAG and HAG calculations compared to traditional methods that use hand-digitized building footprints. The proposed approach offers a solution for comprehensive flood risk management endeavors.

This study assesses the performance of a global Local Climate Zone (LCZ) product [1]. We examined the built-type classes of LCZs in three major metropolitan areas within the U.S. A reference LCZ was constructed using a simple rule-based method based on high-resolution 3D building maps. Our evaluation demonstrated that the global LCZ product struggles to differentiate classes that demand precise building footprint information (Classes 6 and 9), and classes that necessitate the identification of subtle differences in building elevation (Classes 4-6). Additionally, we identified inconsistent tendencies, where the distribution of classes skews differently across different cities, suggesting the presence of a data distribution shift problem in the machine learning-based LCZ classifier. Our findings shed light on the uncertainties in global LCZ maps, help identify the LCZ classes that are the most challenging to distinguish, and offer insight into future plans for LCZ development and validation.

2D and 3D building maps provide essential information for understanding urbanization and diverse geospatial applications. Airborne laser scanning (ALS) is known to be an effective method for 2D and 3D building mappings. Despite numerous efforts to develop automated and accurate building extraction algorithms for ALS, several challenges remain for reliable large-area building mappings. The 30th ACM SIGSPATIAL 2022 held a competition (GISCUP 2022) for large-area building mapping using ALS data. This paper illustrates the implementation of the algorithm that won first place at GISCUP 2022. In addition, we describe some critical issues with large-area building mappings and evaluation methods. Specifically, issues related to ground-truth, building boundaries, and the selection of evaluation metrics (e.g. Intersection over Union) were discussed.