# Quantifying urban solar potential losses from rooftop superstructures via aerial imagery and Convolutional Neural Networks

Alessia Boccalatte, Jocelyn Chanussot

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## To cite this version:

Alessia Boccalatte, Jocelyn Chanussot. Quantifying urban solar potential losses from rooftop superstructures via aerial imagery and Convolutional Neural Networks. Renewable Energy, 2025, 249, pp.123088. 10.1016/j.renene.2025.123088. hal-05084442

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https://hal.science/hal-05084442v1

Submitted on 26 May 2025

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Highlights

Quantifying Urban Solar Potential Losses from Rooftop Superstructures
via Aerial Imagery and Convolutional Neural Networks

Alessia Boccalatte, Jocelyn Chanussot

* U-Net ResNet-152 segments roof superstructures of 35,000 buildings

* Auto-labeled Geneva dataset yields 85% accuracy at the rooftop level

* Removing obstructed roof segments decreases solar potential by 43%

* Public code and model enable reproducibility and adaptation nationwide

* Model's predictions guide more realistic large-scale solar rooftop assessments
Quantifying Urban Solar Potential Losses from Rooftop Superstructures via Aerial Imagery and Convolutional Neural Networks

Alessia Boccalatteª, Jocelyn Chanussotª

ªUniversité Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, 38000, France,

**Abstract**

Accurately estimating the available rooftop area for solar power production systems is crucial for precise assessments of urban solar potential. A primary challenge in this estimation is the presence of rooftop superstructures (e.g., chimneys, heating, ventilation, and air conditioning systems) that hinder installations. In this research, we develop a deep learning-based approach to identify rooftop obstructions using aerial imagery of approximately 35,000 buildings within the Canton of Geneva. Automatic labeling techniques are used to generate training and testing datasets. A semantic segmentation model, based on a U-Net architecture with a ResNet-152 backbone, detects superstructures, achieving an Intersection over Union (IoU) of 0.40. Subsequently, a binary classification of the roof segments into "free" or "obstructed" categories is performed based on the presence of superstructures, achieving an accuracy of 85%. Cross-validation on 1,500 manually labeled buildings in Switzerland yields an accuracy of 91%. The results show that excluding roof segments with superstructures reduces the estimated solar potential by an average of 43%, ranging from 31% in suburban areas to 51% in urban centers. This significant reduction,

Email address: alessia.boccalatte@inria.fr (Alessia Boccalatte)

Preprint submitted to Renewable Energy

March 14, 2025
along with the variability across different urban zones, highlights the importance
of integrating obstruction detection into urban solar potential estimations through
scalable and automated solutions.

Keywords: Deep Learning; Aerial Imagery; Rooftop Superstructures; Semantic Segmentation; Urban Solar Potential Estimation

# 1. Introduction

Effectively assessing the solar potential at the city scale is essential to integrate solar energy production systems (photovoltaic and thermal) into urban environments. This integration represents a crucial step towards achieving nearly zero energy buildings [1], improving energy security [2], and achieving energy transition goals in modern cities [3, 4]. Despite consuming about 70% of global final energy consumption [5], cities often have limited solar availability due to dense urban morphology [6, 7]. In this context, estimating the productivity of roof surfaces becomes particularly important, as they generally feature large areas and higher energy yields compared to vertical surfaces.

Traditionally, Building Energy Simulation Models (BEMs) have been used to estimate the solar potential of individual buildings or small groups of them [8]. However, advances in research, computational resources, and especially the availability of remotely sensed data have enabled the creation of detailed solar cadasters at urban and national scales [9, 10, 11, 12]. These cadasters compute and visualize rooftop energy potential, typically reported as the annual cumulative solar irradiance per square meter. They are crucial for preliminary assessments of urban solar feasibility, site suitability, and system optimization [13]. In addition to these technical advancements, artificial intelligence (AI)-driven approaches have been explored to facilitate solar policy development at the city level [14].
Solar cadasters are generally built from Digital Surface Models (DSMs), which are generated using LiDAR (Light Detection And Ranging) point cloud data [15, 16, 17]. Although DSMs capture the height of terrain and structures in a raster-like format, they do not explicitly define the outlines or shapes of buildings, which are essential for accurate solar potential calculations. To address this gap, DSMs are integrated with building footprint information to construct three-dimensional (3D) urban models at various Levels of Detail (LoD) [18]. LoD1 models depict buildings as simple, block-like volumes with flat rooftops, whereas LoD2 models provide generalized roof configurations, capturing basic slopes and orientations derived from the DSM. After creating these 3D models, standard solar radiation models are applied for tilted surfaces [19], along with shadow casting routines that consider shading from topographical features, vegetation, and other urban surfaces [20].

Despite the increased detail in LoD2 models, they omit small-scale rooftop features such as chimneys, heating, ventilation, and air conditioning (HVAC) units, skylights, dormers, and other architectural elements [21]. These rooftop superstructures reduce usable area and introduce shading, leading to an overestimation of the solar potential when neglected. Capturing these features would require Level of Detail 3 or 4. However, these models are extremely rare because generating them involves significant manual effort and computational resources, making them impractical for large-scale analyses [22].

## 1.1. Assessing Available Rooftop Surface for Solar Energy Integration: State of the Art

Given the limitations of current models in accounting for rooftop superstructures, considerable efforts have been made to enhance solar potential estimates by assessing the usable rooftop surface for solar integration. For large-scale analyses, meth-
ods employing coefficients based on statistical data have been widely used [23, 24]. Schallenberg-Rodríguez et al. [25] and the National Renewable Energy Laboratory (NREL) [26] have conducted comprehensive literature reviews of rooftop utilization coefficients derived from construction types and rooftop geometries, indicating that suitable rooftop surfaces can range from 15% to 65% of the total rooftop area. While these methods are straightforward and generalizable, the substantial variation in results demonstrates low accuracy and neglects localized rooftop characteristics, making validation difficult. Advancements in high-resolution satellite imagery and Geographic Information System (GIS) data have enabled more precise rooftop availability assessments. For example, Ordóñez et al. [27] estimated rooftop availability in Andalusia, Spain, by manually evaluating suitable rooftop areas of a representative sample using aerial imagery. However, despite the improved accuracy, manual evaluation is impractical for large-scale analyses.

More recently, image recognition techniques have been applied to identify available rooftop areas from aerial images. Mainzer et al. [28] developed a method using Canny Edge Detection [29] and the Hough Transformation algorithm [30] to identify rooftop outlines and superstructures, including chimneys and windows. While effective in areas of moderate building density, its applicability to high-density cities with complex rooftop configurations remains unverified. Meanwhile, Convolutional Neural Networks (CNNs) have gained attention for tasks such as rooftop outline identification [31, 32, 33, 34], determining roof segment orientations [35, 36, 37, 38, 39], and identifying existing photovoltaic (PV) installations [40, 41, 42, 43]. CNNs are a specialized type of deep neural network designed to process grid-like data (e.g., images) by learning hierarchical patterns through the use of convolutional filters. At earlier layers, these filters capture simple features such as edges and corners; deeper layers then combine these simpler features to recognize more complex shapes and
objects. This makes CNNs highly effective for image classification, object detection, and semantic segmentation. While identifying PV installations with CNNs is relatively straightforward due to their strong visual contrast with rooftop textures, detecting rooftop superstructures is far more challenging. These features (chimneys, dormers, HVAC units) often share similar textures and colors with the roof surface, resulting in low contrast and making them more difficult for the network to discern.

To date, only one study has employed CNNs specifically for identifying rooftop superstructures. Krapf et al. [36] trained a U-Net model on a manually annotated dataset comprising high-resolution aerial images from 1,880 buildings in the village of Wartenberg, Germany, categorizing rooftop superstructures into eight classes. Their results showed Intersection over Union (IoU) values of approximately 0.7 for PVs and dormers, but for other superstructures, IoU scores were below 0.39. A follow-up study by Li et al. [44] tested the transferability of this method in Munich, Germany, but IoU metrics for superstructures fell below 0.20, highlighting its limited applicability to regions with characteristics differing significantly from the original training set.

For large-scale assessments, Assouline et al. [45] employed a Random Forest model to estimate suitable rooftop areas for photovoltaic installations across Switzerland. Using detailed superstructure data from the Canton of Geneva and 73 urban features as inputs, their method produced results on a 200 m × 200 m grid. While this approach effectively estimates solar potential at a national scale, its reliance on grid-based outputs (200 m × 200 m) prevents localized assessments. As a result, it cannot identify specific rooftop segments occupied by superstructures, limiting its utility for detailed urban solar planning and optimization.
1.1.1. *Research Novelty, Contributions, and Paper Structure*

To address the limitations of existing approaches described above, this study in-
troduces a novel workflow that leverages a detailed rooftop dataset from the Canton
of Geneva, containing precise superstructure information for approximately 35,000
buildings. This dataset is used to create an automatically labeled training set for a
Convolutional Neural Network (CNN) based on a U-Net architecture with a ResNet-
152 backbone. Unlike prior work relying on small, manually labeled datasets with
limited transferability, the proposed approach demonstrates that starting with a
relatively small but highly detailed dataset allows for scalable analyses at the na-
tional level. By ensuring consistency between aerial data and rooftop features across
different regions, the method can identify specific rooftop segments occupied by su-
perstructures with high transferability and localized accuracy.

The principal novelties of this study can be summarized as follows:

* It constitutes the first large-scale, quantitative assessment of how rooftop superstructures reduce the usable surface for solar installations, revealing an average 43% decrease in the estimated solar potential within the selected case study.

* It exploits a detailed and automatically labeled dataset, overcoming limitations imposed by smaller, manually annotated samples used in earlier research.

* It achieves robust segmentation and classification performance (85–91% accu-
  racy) by employing a U-Net with a ResNet-152 backbone to detect rooftop
  with obstructions in aerial imagery.

* It presents a scalable methodology that can be extended from municipal to na-
tional levels while maintaining reliable accuracy across diverse urban contexts.
* It provides an open-source framework (via a public Git repository [46]), enabling reproducibility, validation, and adaptation of the approach in different regions.

In summary, the proposed methodology unifies advanced CNN-based segmentation with a large-scale, automatically labeled dataset, providing robust identification of rooftop superstructures and quantification of their impact on solar potential. While this study focuses on Switzerland, the approach can be adapted to any region offering comparable aerial imagery and roof-feature consistency.

In order to present this contribution comprehensively, Section 2 details the methodological framework, from data collection and preprocessing to the CNN architecture, training procedure, and the quantification of superstructure-induced energy losses. Section 3 then discusses the segmentation performance and associated energy-loss implications, including an evaluation of the model's robustness in unseen regions. Finally, Section 4 summarizes the key findings, addresses current limitations, and proposes future research directions.

## 2. Methodology

This section outlines the methodology for detecting rooftop superstructures and assessing their impact on the solar energy potential. It provides an end-to-end description of the process, from acquiring and preprocessing aerial data to training and testing the Convolutional Neural Network (CNN) used to classify each pixel of a building-centered image as either background, rooftop, or superstructure. Section 2.1 details the input high-resolution aerial imagery and vector data from the Canton of Geneva, as well as the steps used to generate an automatically labeled dataset of approximately 35,000 buildings. This dataset is partitioned into training and testing
subsets in Section 2.2, ensuring representation of diverse urban morphologies. Section 2.3 describes the CNN architecture and training setup, with a U-Net model based on a ResNet-152 backbone. Section 2.4 then explains how segmentation accuracy is evaluated at both pixel and rooftop-segment levels, and describes the procedure for calculating energy losses attributable to superstructure-occupied surfaces. Finally, Section 2.5 covers the testing of the trained model in three communes within the Canton of Geneva, addressing practical concerns and measuring performance, as well as cross-validation in Canton Vaud to assess generalization capabilities to previously unseen regions.

Figure 1 schematically illustrates these steps. Aerial imagery and vector data are first converted into building-centered inputs for the segmentation model, producing an output mask that identifies superstructures on each rooftop. Based on these predictions, each rooftop segment is classified as free or obstructed, and segments containing superstructures are removed from subsequent solar potential calculations, yielding an adjusted estimate of usable rooftop area.

## 2.1. Data Collection and Preprocessing

This section describes the preparation of the labeled dataset used to train and test the Convolutional Neural Network (CNN), which classifies each pixel in an image into one of three categories: background, rooftop, or superstructure. High-resolution aerial images constitute the visual input to the model, while vector datasets provide the spatial information necessary to generate pixel-level ground truth labels.

### 2.1.1. Input Data

Three primary data sources, combining raster and vector formats, are used to create the labeled dataset for training the segmentation model: aerial RGB (Red Green Blue) orthophotos, rooftop section geometries, and rooftop superstructure
<figure><img src="image_3.png" /><figcaption>Figure 1: Overview of the methodology. From left to right: Aerial imagery and vector data are processed into building-centered inputs for the CNN segmentation model. The model's predictions identify rooftop superstructures, allowing each rooftop segment to be classified as free or obstructed. Finally, segments containing superstructures are excluded from the solar potential calculation, resulting in an adjusted estimate of usable rooftop area.</figcaption></figure>

data. The aerial RGB orthophotos, obtained from SwissTopo (SWISSIMAGE 10 cm) [47], are raster images with a resolution of about 10 cm per pixel. These images provide the visual input that the model processes during training and inference.

The rooftop section geometries, derived from SwissTopo's national 3D building model (LoD 2) [48], are vector data representing the shape of each roof section across Switzerland. However, these vectors do not include details on rooftop obstructions.

Detailed superstructure data are available exclusively for the Canton of Geneva. They are provided as vector data by the cantonal geoinformation service (SITG) [49]. This dataset, derived from a high-detail 3D building model (LoD 4) constructed using LiDAR data from 2019 (25 points/m² density, ±10 cm altimetric accuracy, and 20 cm planimetric accuracy), includes vector polygons representing significant obstructions
such as heating, ventilation, and air conditioning (HVAC) units and dormers. These objects are generally large enough to obstruct solar installations or cast substantial shadows, thereby limiting installations on the remaining rooftop areas. Building-integrated features like windows and existing solar panels, as well as very small superstructures with a surface area below 1 m² (e.g., some chimneys), are not included within the dataset, as their impact on solar energy production is minimal. Moreover, superstructures with a surface area exceeding 9 m² are considered as rooftop parts and are thus treated as potentially usable. This classification ensures that only those features that truly obstruct the available rooftop area are excluded from the solar potential calculations, aligning with our objective of realistically estimating urban solar potential.

The Canton of Geneva contains approximately 75,000 buildings. However, the dataset frequently lacks complete superstructure annotations for rooftops with no identified obstructions. To ensure data reliability, it is filtered to retain about 35,000 rooftops that contain at least one superstructure. This selection aligns with previous studies noting omissions in the superstructure dataset [50]. Considerable misalignment between the vector data and aerial imagery is also commonly observed, potentially affecting segmentation performance. Although such inaccuracies are not uncommon in large-scale remote sensing datasets, this filtered subset is deemed sufficiently accurate and extensive to support robust CNN training and validation.

### 2.1.2. Vector Preprocessing

To ensure accurate labeling for training the model, preprocessing is applied to the superstructure vector dataset. A 1-meter buffer is added to the superstructure geometries to account the misalignment frequently occurring with the aerial images and to include shadows cast by the obstructions, which aids in feature recognition
by the network. Trial tests with buffer sizes of 0.5 m, 1 m, and 1.5 m determined
that the 1-meter buffer produced the best segmentation performance.

No buffering is applied to rooftop geometries, as their larger size makes them
less sensitive to positional shifts. Care is taken to ensure that the rooftop and
superstructure masks do not overlap, preserving distinct class boundaries for training.

### 2.1.3. Automatic Labeling Workflow

A Python-based labeling process combines the raster and vector datasets to pro-
duce the final image-mask pairs for CNN training. First, the original aerial orthopho-
tos from SwissTopo [48], provided in TIFF format, are cropped into 256×256 pixel
tiles, each centered on a building's centroid. Next, the corresponding rooftop and
buffered superstructure geometries are rasterized to form pixel-level masks aligned
with these tiles. Every pixel in the mask is assigned to one of three classes: back-
ground (areas outside the roof), rooftop (the roof section), or superstructure (the
buffered obstruction). These image-mask pairs are saved in PNG format to provide
the ground truth labels required for supervised learning.

During training, the network ingests a 256×256 aerial tile and learns to predict
the class of each pixel by comparing its output to the ground truth mask. After
optimization, the trained model can segment new tiles of the same size, assigning each
pixel to background, rooftop, or superstructure. Figure 2 provides an overview of this
workflow, showing how the original aerial imagery and vector data are transformed
into inputs for the network, and how the model subsequently generates segmentation
masks.

## 2.2. Dataset Splitting and Urban Morphology Selection

The datasets for training and testing the network are created using geographically
separated areas within the Canton of Geneva. The training set includes all municipal-
<figure><img src="image_4.png" /><figcaption>Figure 2: Workflow for data preprocessing, labeling, and CNN segmentation. RGB aerial images are cropped into 256×256 pixels, centered on each building. Rooftop vectors and superstructure vectors (buffered by 1 meter) are rasterized to generate pixel-based ground truth masks (background, rooftop, superstructure). During inference, the trained CNN outputs its own segmentation masks for new images, classifying each pixel into one of the three classes.</figcaption></figure>

ities in the canton except for three selected test municipalities: Collonge-Bellerive, Genève-Petit-Saconnex, and Carouge. To ensure a clear separation between the training and testing datasets, buildings located along municipal boundaries are removed to exclude any potential overlap between training and testing data. The training set accounts for approximately 84% of the dataset (29,191 examples), while
the testing set comprises 16% (5,470 examples), ensuring a sufficient representation
in both subsets.

The test municipalities are chosen to represent three distinct urban morphologies
that reflect the different urban textures of the Canton of Geneva. This ensures that
the testing set adequately encompasses the diverse range of rooftop superstructures
associated with different building types. Collonge-Bellerive, a low-density residential
area characterized by detached single-family buildings with simple gable or hipped
roofs, primarily features superstructures such as chimneys and dormers. Genève-
Petit-Saconnex, a mixed-use commercial and industrial zone with large flat roofs, is
characterized by service obstructions such as HVAC units. Carouge, a high-density
area near the city center with building blocks aggregated into complex courtyard or
block structures, presents a mix of architectural features, including large chimneys,
dormers, and varied rooftop elements.

Figure 3 illustrates the Canton of Geneva, with training municipalities shown in gray, test municipalities highlighted in red, and examples of the three urban typologies depicted through aerial cutouts.

## 2.3. *Model Architectures and Training Setup*

This section outlines the neural network architectures evaluated for rooftop superstructure segmentation and the training protocols used. It first discusses the tested architectures and their selection rationale, followed by the final model and its training configuration for optimal performance.

### 2.3.1. *Selection and Evaluation of Different Model Architectures*

Detecting rooftop superstructures in high-resolution aerial imagery is a semantic
segmentation task, where each pixel must be classified as background, rooftop, or
<figure><img src="image_5.png" /><figcaption>Figure 3: Map of the Canton of Geneva showing the municipalities used for training (gray) and testing (red). The cutouts illustrate the three urban typologies represented by the testing set: (top) low-density residential buildings in Collonge-Bellerive, (middle) mixed-use commercial and industrial buildings in Genève-Petit-Saconnex, and (bottom) high-density building blocks aggregated into courtyard or block structures in Carouge.</figcaption></figure>

superstructure. Superstructures can be very small, exhibit varied shapes, and re-
semble surrounding rooftop textures, making them difficult to isolate. To address
these issues, two state-of-the-art convolutional neural network (CNN) architectures
are evaluated: U-Net and DeepLabV3+. Both architectures use learnable filters that
convolve over images to extract features at various levels of abstraction, allowing the
model to progressively identify more complex structures at deeper layers, ultimately producing a segmentation mask that matches the spatial resolution of the input image.

U-Net [51] employs an encoder-decoder design in which the encoder compresses the input into lower-resolution feature maps, while the decoder restores spatial resolution to produce a pixel-wise classification. The encoder and decoder are connected by skip connections that transfer high-resolution features directly from early encoding layers to corresponding decoding layers, thus preserving spatial detail and improving the network's ability to detect small or subtle objects. This symmetric U-shaped structure is tested with three ResNet backbones, namely ResNet-34, ResNet-50, and ResNet-152 [52]. ResNet backbones incorporate residual learning to stabilize training in deeper networks and mitigate vanishing gradients. By varying the depth of the backbone, a trade-off between computational efficiency and segmentation performance is assessed.

DeepLabV3+ [53] builds upon DeepLabV3 by adding a decoder module that refines object boundaries, and uses dilated convolutions in the encoder to capture multi-scale contextual information without excessive downsampling. This approach is suitable for identifying rooftop superstructures of different sizes and shapes, as it enables the model to integrate fine-grained features with broader contextual cues. The decoder module further enhances boundary delineation, improving segmentation accuracy. DeepLabV3+ is implemented with a ResNet-101 backbone [52], balancing depth for feature extraction with computational feasibility.

Alternative architectures, including Fully Convolutional Networks (FCNs) [54], SegNet [55], and Pyramid Scene Parsing Network (PSPNet) [56], were initially considered but ultimately not adopted. While certain newer or more complex models might incrementally improve pixel-level IoU scores for superstructure segmentation,
they typically involve specialized training pipelines, extensive hyperparameter tuning, and considerable computational overhead. Such factors can hinder reproducibility for researchers and practitioners in the renewable energy domain, who often value accessible tools over cutting-edge architectures. Specifically, FCNs, despite integrating skip connections, use an additive fusion approach that may fail to preserve small-scale spatial information as effectively as U-Net. SegNet, designed for efficiency and memory conservation, lacks skip connections between encoder and decoder, potentially limiting its capacity to capture subtle rooftop features. PSPNet, leveraging pyramid pooling for global context, may underperform when detecting small or localized elements. By contrast, U-Net and DeepLabV3+ are both widely recognized in remote sensing, straightforward to implement, and sufficiently robust for our principal aim of distinguishing obstructed from unobstructed rooftops.

### 2.3.2. Final Architecture and Training Protocol

Following the evaluation of the different model architectures, the U-Net with a ResNet-152 backbone is selected as the final model due to its superior performance (detailed results are presented in Section 3.1). The network is initialized with weights pretrained on ImageNetV2 [57]. The training protocol is designed to optimize the performance of the model while addressing the class imbalance. A weighted cross-entropy loss function is employed to focus on the underrepresented superstructure class. Specifically, the loss function $\mathcal{L}$ for a single input image is defined as in Equation 1:

$$ \mathcal{L} = -\frac{1}{N} \sum_{i=1}^{N} \sum_{k=1}^{C} w_k y_{i,k} \log(p_{i,k}), \quad (1) $$

where $N$ is the number of pixels in the image, $C = 3$ is the number of classes (background, rooftop, superstructure), $y_{i,k}$ is the ground truth label for pixel $i$ and
class k (1 if pixel i belongs to class k, 0 otherwise) $p_{i,k}$ is the predicted probability that pixel i belongs to class k, $w_k$ is the weight assigned to class k.

To address the class imbalance and emphasize the underrepresented superstructure class, a progressive weighting scheme is employed during training. The weight for the superstructure class, $w_{super}(e)$, increases linearly across epochs from an initial value of $w_{initial} = 5$ to a maximum value of $w_{max} = 50$, as defined in Equation 2:

$$w_{super}(e) = w_{initial} + \left(\frac{e}{E}\right) (w_{max} - w_{initial}), \qquad (2)$$

where $e$ represents the current epoch, and $E=50$ is the total number of epochs. This strategy ensures a gradual increase in focus on the superstructure class, which constitutes only 2.5% of the total pixels, without causing instability. Class weights are set based on the inverse of class frequencies. The initial weight for the superstructure class $w_{initial} = 5$ reflects the rarity of this class while ensuring stable gradients. The maximum weight $w_{max} = 50$ is chosen to sufficiently emphasize the superstructure class without overfitting or degrading performance on the other classes. The model is trained using the Adam optimizer [58] with an initial learning rate of $1 \times 10^{-4}$. A learning rate scheduler reduces the learning rate by a factor of 0.8 if the validation loss does not improve for five consecutive epochs. To mitigate overfitting, weight decay with a coefficient of $1 \times 10^{-5}$ and a dropout rate of 0.4 [59] are applied. Training is conducted over 50 epochs with a batch size of 8. All hyperparameters are manually fine-tuned based on empirical observations to achieve optimal performance. Experiments are conducted on a single NVIDIA A5000 GPU with 24 GB of memory, utilizing the PyTorch framework for implementation [60].
## 2.4. Model Performance Evaluation and Energy Loss Assessment

The impact of rooftop superstructures on solar potential is assessed by evaluating segmentation model performance and quantifying losses in usable rooftop area and energy production. This section outlines the metrics for segmentation accuracy and the methodology for estimating area reduction and energy losses.

### 2.4.1. Evaluation Metrics for Model Performance

The performance of the segmentation model in detecting rooftop superstructures is evaluated at both the pixel level and the rooftop segment level.

At the *pixel level*, standard semantic segmentation metrics, namely Intersection over Union (IoU), Precision, Recall, F1 Score, and Accuracy are computed for each class k (background, rooftop, superstructure). The well-known equations defining these metrics are provided in Appendix A. The performance metrics are calculated for each class individually and then averaged to obtain the mean metrics. The best model is selected as the one with the highest mean IoU over the three classes.

However, the IoU for the *superstructure* class is relatively low due to several factors: the small size and sparse distribution of superstructures, technical limitations in small object segmentation, and dataset issues such as misalignment and incomplete labeling (as discussed in Section 2.1). Minor deviations in predictions can significantly impact overlap with the ground truth, leading to low IoU values, consistent with literature findings where IoU scores for rooftop superstructures (excluding existing PV modules) often fall below 0.4 [44, 36, 37]. These results highlight the model's limited ability to reproduce precise superstructure geometries rather than its capacity to detect their presence, necessitating a shift from the micro (pixel) scale to the macro (rooftop segment) scale for evaluation.

The International Energy Agency (IEA) defines "architectural suitability" [61]
as rooftops free from obstructing objects or alternative uses, including fixed super-
structures (e.g., dormers, chimneys, elevators), roof-mounted structures (e.g., HVAC
equipment), windows, roof terraces, gardens, protected roofs, and shaded areas. Con-
sequently, the evaluation of the model shifts to determining whether a rooftop seg-
ment contains superstructures or not.

To address this, the performance of the model is evaluated at the rooftop segment level. Each rooftop segment is classified as either containing superstructures or not, based on predictions. The workflow for classifying rooftop segments from superstructure predictions is illustrated in Figure 4. Initially, the raw superstructure predictions generated by the model (white contour) are processed by removing the 1-meter buffer added during the preprocessing phase (Section 2.1.2), resulting in unbuffered predictions (yellow). Predictions with an area smaller than 1 m² are then removed to exclude minor and negligible features. Finally, rooftop segments are classified as either containing superstructures (red) or not containing them (green).
<figure><img src="image_6.png" /><figcaption>Figure 4: Classification of rooftop segments based on model predictions of superstructures. Raw predictions (white contours) are unbuffered (yellow). Rooftop segments are then classified as containing superstructures (red) or not containing them (green).</figcaption></figure>

The results of the classification are then compared with ground truth, and the same metrics used at the pixel level (i.e. Precision, Recall, F1 Score, and Accuracy), excluding IoU, are calculated at the segment level, using the counts of True Positives (TP), False Positives (FP), True Negatives (TN), and False Negatives (FN).

This approach aligns with practical considerations for solar installations, where the presence of superstructures significantly affects feasibility. The superstructures predicted by the model are primarily those with substantial surface areas and significant height above the rooftop plane, making them likely to cast considerable shading
(as detailed in Section 2.1.1). While it is technically feasible to design solar installations in the remaining spaces between obstructive features, the available area in such segments is typically so reduced and fragmented that their utility for large-scale solar potential assessments becomes negligible. Considering the characteristics of the superstructures identified by the model, their exclusion is deemed appropriate for preliminary estimates aimed at identifying rooftops with high potential for solar installations.

### 2.4.2. Quantifying Rooftop Area Losses

The quantification of energy losses due to rooftop superstructures involves evaluating the reduction in usable rooftop area and estimating the corresponding decrease in potential solar energy production.

The cumulative annual solar irradiance ($I_s$) expressed in kWh/m² per year and the real rooftop area ($A_{Total,s}$) are precomputed for each rooftop segment and provided as attributes in the official solar cadaster for Geneva [48]. The cadaster leverages multi-year LiDAR analyses and 3D building geometry to account for slope, orientation, and local shading. The potential annual energy production ($E_{Total,s}$) for a rooftop segment $s$ is calculated using Equation 3:

$$E_{Total,s} = I_s \cdot A_{Total,s} \qquad (3)$$

where $E_{Total,s}$ is the total potential energy production (kWh per year), $I_s$ is the cumulative annual solar irradiance on segment $s$ (in kWh/m² per year), and $A_{Total,s}$ is the total area of segment $s$ (in m²).

To quantify the impact of superstructures, rooftop segments containing these features are excluded from the usable area calculation. The adjusted energy production ($E_{Adjusted}$) is calculated as the sum of the energy production potential for only the
usable segments, as shown in Equation 4:

$$E_{\text{Adjusted}} = \sum_{s \in S_{\text{Usable}}} (I_s \cdot A_{\text{Total},s}) \quad (4)$$

where $S_{Usable}$ represents the set of rooftop segments deemed suitable for solar installations, i.e. without any superstructure. The total energy loss ($\Delta E$) is determined using Equation 5:

$$\Delta E = \sum_{s \in S_{\text{Excluded}}} (I_s \cdot A_{\text{Total},s}) \quad (5)$$

where $S_{Excluded}$ represents the set of segments classified as containing superstructures.

The percentage energy loss relative to the total potential energy production is then calculated using Equation 6:

$$\text{Energy Loss (\%)} = \left( \frac{\Delta E}{\sum_{s \in S} (I_s \cdot A_{\text{Total},s})} \right) \times 100, \quad (6)$$

where $S$ is the set of all rooftop segments considered in the analysis.

## 2.5. Testing and Cross-Validation

To comprehensively evaluate the performance of the segmentation model and its ability to generalize across regions, two evaluation phases are conducted: testing in designated communes within the Canton of Geneva and cross-validation in Lausanne, Canton of Vaud.

### 2.5.1. Performance Testing in Geneva

As outlined in Section 2.2, the testing phase focuses on three communes in Geneva (Petit-Saconnex, Carouge, and Collonge-Bellerive) which represent diverse urban
morphologies. These areas are used to evaluate the model's performance at both the *pixel level* and the *rooftop segment level*, as described in Section 2.4.1. Furthermore, energy losses due to superstructures are calculated for each commune (Section 2.4.2) to assess how urban morphology influences the potential solar energy production. This testing phase provides a detailed assessment of the model's segmentation quality and its practical implications for solar energy evaluations.

### 2.5.2. *Cross-Validation in Lausanne*

To evaluate the capability of the segmentation model to generalize to unseen regions, cross-validation is conducted in Lausanne, Canton of Vaud, where detailed superstructure labels are unavailable. Instead of creating precise annotations for individual superstructures, which involves delineating the geometries and generating polygonal footprints for each superstructure (which is a very time-intensive process), the binary labeling approach is adopted, classifying each rooftop segment as either containing or not containing superstructures.

The Prilly neighborhood near Lausanne's center is chosen for its diverse urban morphology including isolated residential buildings, row houses, and larger block structures with flat roofs. Approximately 1,500 rooftop segments are manually labeled within a few hours.

The predictions of the model are finally compared with this manually created ground truth, and metrics such as Precision, Recall, F1 Score, and Accuracy are computed to assess performance.

## 3. Results and Discussion

This section presents the results of the segmentation performance and the impact of rooftop superstructures on solar potential. Section 3.1 evaluates and compares
the segmentation performance of the CNN model across the selected architectures.
Section 3.1.1 illustrates example predictions of superstructure geometries from the
testing dataset to visually assess the model's outputs. Section 3.2 discusses the per-
formance at the rooftop segment level and quantifies the energy losses attributable to
superstructures. Finally, Section 3.3 examines the model's generalization capabilities
through cross-validation in unseen regions.

3.1. *Segmentation Performance Comparison Across the Selected Model Architectures*

This section provides a detailed analysis of the segmentation performance of the
four selected architectures: U-Net ResNet-34, U-Net ResNet-50, U-Net ResNet-152,
and DeepLabV3+ ResNet-101.

Table 1 summarizes the results in terms of Intersection over Union (IoU), Precision, and F1 Score at the pixel level for the three target classes: *Superstructures* (*Super*), *Rooftops* (*Roof*), and *Background* (*Bg*). The overall Mean IoU is also presented to provide a comparative overview of each model’s performance.

The segmentation of superstructures is the most challenging task for the models
due to their small size, geometrical heterogeneity, and sparse distribution. U-Net
ResNet-152 achieves the highest IoU for superstructures (0.401), followed by U-Net
ResNet-50 (0.383). U-Net ResNet-34 and DeepLabV3+-ResNet-101 have compara-
ble and lowest IoU values for this class (0.368 and 0.367, respectively). The superior
IoU of U-Net ResNet-152, about the 5% higher than the other architectures, demon-
strates its ability to capture fine-grained details and the deeper backbone results
capable of learning precise spatial features essential for distinguishing small objects.
Concerning Precision, the highest value i reached with DeepLabV3+ ResNet-101
(0.601), suggesting that it effectively minimizes false positives, potentially by miss-
ing some true positives, as evidenced by its lower F1 Score (0.536) compared to
<table><caption>Table 1: Comparison of model architectures across key metrics. Best values for each metric are highlighted in **bold**. Super, Roof, and Bg stand for Superstructures, Rooftops, and Background, respectively.</caption><thead><tr><th>Metric</th><th>U-Net<br>ResNet-152</th><th>U-Net<br>ResNet-50</th><th>U-Net<br>ResNet-34</th><th>DeepLabV3+<br>ResNet-101</th></tr></thead><tbody><tr><td>Super IoU</td><td><strong>0.401</strong></td><td>0.383</td><td>0.368</td><td>0.367</td></tr><tr><td>Super Precision</td><td>0.582</td><td>0.506</td><td>0.483</td><td><strong>0.601</strong></td></tr><tr><td>Super F1</td><td><strong>0.571</strong></td><td>0.554</td><td>0.538</td><td>0.536</td></tr><tr><td>Roof IoU</td><td><strong>0.756</strong></td><td>0.740</td><td>0.726</td><td>0.749</td></tr><tr><td>Roof Precision</td><td>0.856</td><td><strong>0.866</strong></td><td>0.864</td><td>0.863</td></tr><tr><td>Roof F1</td><td><strong>0.861</strong></td><td>0.850</td><td>0.842</td><td>0.857</td></tr><tr><td>Bg IoU</td><td>0.932</td><td><strong>0.933</strong></td><td>0.929</td><td>0.931</td></tr><tr><td>Bg Precision</td><td><strong>0.966</strong></td><td>0.964</td><td>0.960</td><td>0.957</td></tr><tr><td>Bg F1</td><td>0.965</td><td><strong>0.965</strong></td><td>0.963</td><td>0.964</td></tr><tr><td><strong>Mean IoU</strong></td><td><strong>0.696</strong></td><td>0.685</td><td>0.675</td><td>0.682</td></tr></tbody></table>

U-Net ResNet-152 (0.571).

The performance gap between DeepLabV3+ ResNet-101 and U-Net models likely stems from architectural differences. DeepLabV3+ tends to emphasize the global context through dilated convolutions, while effective for capturing broader patterns, limits its ability to handle small, irregular objects like superstructures. This contrast is less pronounced for rooftops, where DeepLabV3+ ResNet-101 achieves an IoU (0.749) comparable to U-Net ResNet-152 (0.756), reflecting the robustness of all architectures in segmenting larger, well-defined features.

The background class shows minimal variation across architectures, with all mod-
els achieving IoU values equal to or above 0.93.

Although the Mean IoU across all classes is comparable, the superior performance of U-Net ResNet-152 for superstructures aligns with the research focus and justifies its selection as the final model.

### 3.1.1. *Segmentation Examples*

Figure 5 illustrates the segmentation predictions for the three classes (superstructures in red, rooftops in yellow, and background in blue) across scenes with different urban textures, rooftop geometries and colors, and superstructure types. The IoU values for each class are also provided to assess quantitative performance.

Scene (a) depicts a white flat roof featuring squared skylights, most of which are correctly identified by the model. Some inaccuracies are observed along roof edges and shadowed areas, which are occasionally misclassified as background. Scene (b) represents a dense urban texture with reddish-brown rooftops and numerous dormers. The model demonstrates robust segmentation capabilities, correctly identifying the majority of dormers. Scene (c) shows a residential area with gabled gray rooftops. Chimneys in this setting are effectively detected, showcasing the model's adaptability to various rooftop forms and textures.

The IoU values for superstructures, as discussed in Section 2.4.1, remain relatively low (not exceeding 0.65) compared to rooftops. This is attributable to the small size of superstructures and frequent misalignment between aerial images and vector data. These factors also explain the overall pixel-level IoU for superstructures, which does not surpass 0.40 coherently with other literature studies. While such values may seem suboptimal in other contexts, the qualitative results depicted in the examples demonstrate sufficient accuracy for practical applications in large scale solar potential assessments.
<figure><img src="image_7.png" /><figcaption>Figure 5: Segmentation predictions for the three classes (superstructures in red, rooftops in yellow, and background in blue) across different urban textures and rooftop geometries.</figcaption></figure>

Figure 6 illustrates cases where the model predictions diverge from the ground truth, highlighting specific challenges related to the dataset and model behavior. As discussed in Section 2.1.1, the superstructure vector dataset typically excludes elements coplanar with the roof, as these are difficult to extract from LiDAR data. However, such elements are occasionally mislabeled. In scene (d), small roof windows coplanar with the surface are labeled as superstructures. The model correctly
excludes these features, aligning with the rationale that such elements, occupying
minimal space and producing no shading, do not hinder the installation of solar sys-
tems on the corresponding roof segments. Scene (e) illustrates an instance of missing
data in the superstructure dataset. Although the roof features prominent HVAC de-
vices, these are absent in the ground truth but are correctly identified by the model,
demonstrating its capacity to detect large obstructive features not captured in the
vector data. Finally, scene (f) examines the model's handling of existing PV mod-
ules. Integrated modules, which are coplanar with the roof, are absent from both
the ground truth and the model predictions, consistent with the intended objectives.
In contrast, open-rack PV modules, elevated above the roof with a tilt angle, are
identified as superstructures in both the ground truth and predictions. This classifi-
cation is appropriate, as these modules create significant shading similarly to other
superstructures.
<figure><img src="image_8.png" /><figcaption>Figure 6: Segmentation predictions for the three classes (superstructures in red, rooftops in yellow, and background in blue) in challenging cases.</figcaption></figure>

## 3.2. Assessment of Model Performance and Energy Losses Due to Superstructures across the Testing Municipalities

After selecting the final model based on pixel-level metrics and qualitatively confirming its ability to segment superstructures with sufficient accuracy for practical applications, the performance of the model is evaluated at the rooftop segment level in the three testing municipalities. As described in Section 2.4.1, this evaluation
focuses on the model's capacity to classify rooftop segments as containing or not containing superstructures, aligning more directly with large-scale solar potential assessments. The analysis begins with aggregate results across the entire testing area, followed by a municipality-specific comparison to evidence the different impact of superstructures on diverse urban textures.

### 3.2.1. General Results Across the Testing Area

The testing dataset consists of 5,470 buildings, corresponding to 18,136 rooftop segments. According to the ground truth (GT), superstructures are present in 4,129 segments, while the model predicts superstructures in 4,081 segments.

Table 2 presents the key performance metrics at the segment level (Accuracy, Precision, Recall, and F1 Score) derived from the comparison between model predictions and GT labels. These metrics reflect the model's ability to correctly identify superstructure-containing segments (high Recall) while not excessively over-flagging negative segments (moderate Precision), resulting in a balanced F1 Score. The latter, with a value of 66.38%, indicates that the model effectively identifies a substantial portion of true superstructure segments without too many false alarms. In a preliminary large-scale solar assessment context, this balance ensure that major obstructions are rarely missed.

<table><caption>Table 2: Overall segment-level classification metrics for the entire testing area.</caption><thead><tr><td>Metric</td><td>Accuracy (%)</td><td>Precision (%)</td><td>Recall (%)</td><td>F1 (%)</td></tr></thead><tbody><tr><td>Overall</td><td>84.78</td><td>66.77</td><td>66.00</td><td>66.38</td></tr></tbody></table>

To understand the distribution of classification outcomes, Table 3 presents the confusion matrix. The model correctly classifies 15,376 segments (2,725 true positives (TP) and 12,651 true negatives (TN)) but also produces 1,356 false positives
(FP). This relatively high FP count suggests that the model sometimes identifies superstructures in segments not marked as such by the ground truth. This discrepancy largely arises from dataset incompleteness: as previously mentioned, some superstructures detected by the model may indeed exist but were never captured or annotated in the GT. This issue is examined more thoroughly in Section 3.3, where a manually labeled dataset provides a more accurate ground truth for verification.

<table><caption>Table 3: Segment-level confusion matrix for the entire testing area. *TP*: True Positives, *FN*: False Negatives, *FP*: False Positives, *TN*: True Negatives.</caption><thead><tr><th></th><th>Predicted Positive</th><th>Predicted Negative</th></tr></thead><tbody><tr><th>GT Positive</th><td>2,725 (TP)</td><td>1,404 (FN)</td></tr><tr><th>GT Negative</th><td>1,356 (FP)</td><td>12,651 (TN)</td></tr></tbody></table>

Finally, Table 4 presents the overall energy loss calculations. The total annual solar energy potential of all tested segments is approximately 2,800.26 GWh. Excluding segments predicted to contain superstructures reduces the usable potential to 1,592.19 GWh, resulting in a 43.14% energy loss. This substantial reduction underscores the importance of accounting for the influence of superstructures on large scale urban solar potential assessments.

### 3.2.2. Municipality-Specific Results

To further analyze the performance of the model under diverse urban morphologies and to determine the contexts in which it achieves best results, the findings are investigated at the municipality-level. This section provides a focused assessment of Genève-Petit-Saconnex, Carouge, and Collonge-Bellerive individually.

Table 5 presents a comparison of key results (Accuracy, F1 Score, and Energy Loss) for each municipality.
<table><caption>Table 4: Overall energy loss calculations. The Energy Loss (%) indicates the reduction in total solar potential after excluding segments predicted to contain superstructures.</caption><thead><tr><th>Metric</th><th>Value (GWh/year)</th><th>Energy Loss (%)</th></tr></thead><tbody><tr><td>Total Energy Potential</td><td>2,800.26</td><td>-</td></tr><tr><td>Usable Energy Without Superstructures</td><td>1,592.19</td><td>-</td></tr><tr><td>Energy Loss Due to Superstructures</td><td>1,208.07</td><td>43.14</td></tr></tbody></table>

*Genève-Petit-Saconnex:* This municipality, characterized by a mixed-use commercial and industrial setting with extensive flat rooftops often hosting HVAC units, attains an Accuracy of 81.40% and an F1 Score of 61.40%. The comparatively lower F1 Score suggests greater difficulty in consistently identifying all superstructure-containing segments. One plausible explanation lies in the distinctive rooftop features prevalent in this area, which may not be well represented in the training dataset. The underrepresentation of such large, flat, and equipped roofs could limit the model's ability to generalize. In terms of energy potential, Genève-Petit-Saconnex starts with an estimated 1,397.69 GWh of annual solar energy. Excluding segments predicted to contain superstructures reduces usable energy to 781.36 GWh, resulting in a 44.10% loss, in line with the aggregated result.

*Carouge.* The high-density urban blocks of this area lead to an F1 Score of 74.18%, the highest among the three municipalities. This elevated F1 Score implies that within the more complex environment, the model attains a good balance in correctly identifying both positive and negative segments. However, despite this improved F1 Score, the energy loss reaches 51.16% (the highest overall) because a substantial proportion of rooftops contain superstructures. The total energy potential starts at 770.39 GWh and drops to 376.24 GWh post-exclusion.
*Collonge-Bellerive.* The low-density residential morphology of Collonge-Bellerive and simpler rooftop layouts yield the highest Accuracy (87.63%), indicating that the model rarely misclassifies segments in this context. However, the F1 Score (65.69%) is lower than in Carouge. This suggests that while the model is able to classify most segments correctly (leading to high Accuracy), maintaining a balanced detection of superstructures and non-superstructures (as reflected in F1) is more challenging here. Nonetheless, the impact of superstructures on solar potential is the lowest, with an energy loss by 31.25%.

<table><caption>Table 5: Comparison of Accuracy, F1 Score, and Energy Loss across the three testing municipalities. The Energy Loss (%) quantifies the reduction in solar potential after excluding segments predicted to contain superstructures.</caption><thead><tr><td>Municipality</td><td>Accuracy (%)</td><td>F1 (%)</td><td>Energy Loss (%)</td></tr></thead><tbody><tr><td>Genève-Petit-Saconnex</td><td>81.40</td><td>61.40</td><td>44.10</td></tr><tr><td>Carouge</td><td>85.59</td><td>74.18</td><td>51.16</td></tr><tr><td>Collonge-Bellerive</td><td>87.63</td><td>65.69</td><td>31.25</td></tr></tbody></table>

These results evidence that urban morphology and superstructure distribution influence both model performance and solar potential. While the model demonstrates a balanced capability across diverse settings, denser and more complex morphologies yield greater energy losses. Conversely, as expected, simpler urban structures maintain a larger proportion of usable rooftop area.

### 3.3. *Cross-Validation on Unseen, Manually Labeled Data*

The generalization capabilities of the model are further evaluated through a cross-validation experiment on a new manually labeled dataset. Unlike the initial testing dataset, which may contain incomplete or imprecise annotations, this new data
source is both spatially distinct and reliably annotated. Approximately 1,500 buildings, comprising 3,780 rooftop segments, are selected from the Prilly neighborhood near Lausanne (Canton Vaud). Each segment is checked to ensure that all non-coplanar superstructures exceeding 1 m² in surface area are accurately recorded as superstructures.

Table 6 presents the segment-level classification metrics, i.e. Accuracy, Precision, Recall, and F1 Score. In comparison to the earlier testing results (Table 2), Accuracy (90.61%) and Precision (76.28%) improve, indicating fewer false alarms. However, Recall (53.30%) and F1 Score (62.75%) are lower, suggesting that while the model is now more accurate in confirming the presence of superstructures, it also fails to detect a larger fraction of the actual obstructions present.

<table><caption>Table 6: Segment-level classification metrics for the cross-validation on unseen, manually labeled data.</caption><thead><tr><td>Metric</td><td>Accuracy (%)</td><td>Precision (%)</td><td>Recall (%)</td><td>F1 (%)</td></tr></thead><tbody><tr><td>Cross-Validation</td><td>90.61</td><td>76.28</td><td>53.30</td><td>62.75</td></tr></tbody></table>

To place these performance metrics in context, Table 7 provides the corresponding confusion matrix, detailing the True Positives (TP), False Positives (FP), False Negatives (FN), and True Negatives (TN). The majority of segments are correctly classified (3,126 TN and 299 TP), reflecting the improved Precision and Accuracy metrics. Nevertheless, the model also records 262 FN, indicating a substantial number of undetected superstructures. This reduction in Recall, compared to the initial testing scenario, can be attributed to multiple factors, such as the prevalence of smaller, subtler, or differently configured superstructures that may not align well with the patterns learned from the training data.
<table><caption>Table 7: Segment-level confusion matrix for the cross-validation dataset in Prilly (Canton Vaud).

TP: True Positives, FN: False Negatives, FP: False Positives, TN: True Negatives.</caption><thead><tr><th></th><th>Predicted Positive</th><th>Predicted Negative</th></tr></thead><tbody><tr><th>GT Positive</th><td>299 (TP)</td><td>262 (FN)</td></tr><tr><th>GT Negative</th><td>93 (FP)</td><td>3,126 (TN)</td></tr></tbody></table>

Figure 7 illustrates the Prilly test area, showing both ground truth labels (red segments indicate superstructure-containing rooftops, green segments indicate free rooftops) and the model's predictions (yellow outlines represent predicted superstructures) for a selection of buildings. This visualization reveals that most segments are accurately classified, yet certain small or subtle superstructures remain undetected. In some instances, the model also predicts superstructures on rooftops labeled as free, although such cases are less frequent. A qualitative analysis suggests that missed detections often involve very small features or coplanar elements like skylights or rooftop windows, underscoring the model's difficulty in fully capturing the complexity of superstructures variability.
<figure><img src="image_9.png" /><figcaption>Figure 7: An overview of the Prilly cross-validation area (Canton Vaud). Red segments indicate rooftops containing superstructures, and green segments indicate free rooftops according to the manual ground truth. The inset shows a closer view of selected buildings, overlaying the model's predicted superstructures in yellow.</figcaption></figure>

The results of the cross-validation experiment offer a more comprehensive assessment of the model's true detection capabilities. The improved Precision and Accuracy confirm that the manually labeled dataset improves the model's capability of positively identifying superstructures. However, the reduced Recall and F1 Score highlight the challenge of detecting all variations of rooftop features, especially those less represented. From a practical standpoint, these findings remain satisfactory for preliminary assessments. The predictions of the model can be considered strong indi-
cators of meaningful obstructions, yet further refinements in data collection, training approaches, or architectural modifications may be necessary to better capture the diversity and variety of superstructure typologies.

## 4. Conclusions

This study introduces a novel methodology for identifying rooftop superstructures from aerial imagery using a deep learning-based approach and quantifying the related solar potential losses at city and regional scales. By integrating a U-Net model with a ResNet-152 backbone and leveraging detailed, regionally constrained data from the Canton of Geneva, this research provides the first quantitative estimation of the impact of rooftop superstructures on urban solar potential at a large scale. Although the training dataset is relatively small but detailed, the model extrapolates effectively to neighboring regions where rooftop morphology and superstructure types remain broadly consistent with the Geneva context.

On average, excluding segments containing meaningful superstructures reduces the estimated solar potential by approximately 43% in our case study. This reduction varies with urban morphology, ranging from about 31% in suburban areas to 51% in high-density urban centers. Notably, the model performs effectively even in dense, complex environments, which typically pose challenges in similar literature studies. These findings underscore the importance of integrating superstructure detection into large-scale solar potential estimations to avoid substantial overestimation of realistically available rooftop area.

Validation on the testing dataset in the Canton of Geneva shows an accuracy of 85% at the rooftop segment level. A cross-validation experiment in the Prilly neighborhood (Canton Vaud) with an unseen, manually labeled dataset provides a more comprehensive performance assessment. Under these conditions, the model achieves
improved accuracy and precision but exhibits lower recall and F1 Score, reflecting
the persistent challenge of detecting all existing obstructions, especially smaller or
less visually distinctive ones. Nonetheless, these results remain satisfactory for pre-
liminary large-scale assessments. They also illustrate how a localized but precise
training dataset can yield reliable estimates if the target rooftops exhibit compa-
rable characteristics (namely, roof slopes, materials, and superstructure typologies)
thus supporting broader applications of this technique across similarly configured
urban settings.

The results of this study hold significant implications for local solar policy-
making. By accurately identifying rooftops free from significant obstructions, mu-
nicipalities can better define priorities for solar energy incentives and installation
plans. Rather than relying solely on irradiance values, the approach introduced in
this study enables prioritization based on practical ease of installation, driven by
available unobstructed rooftop space. This method allows policy-makers to target
specific areas where installations can be completed more quickly and efficiently, max-
imizing returns on investment and accelerating the adoption of solar energy.

Some limitations persist. Despite careful preprocessing and buffering strategies, minor misalignment or incomplete data can still affect pixel-level segmentation accuracy. While this conservative binary classification excludes all segments containing meaningful superstructures, it remains possible, in some cases, to install solar systems on partially obstructed rooftops (although at reduced productivity due to diminished surface area and shading effects). Moreover, the estimated 43% reduction in solar potential is inherently tied to the specific urban morphology, building characteristics, and rooftop configurations of the Canton of Geneva. These results should not be directly extrapolated to areas with significantly different architectural styles, construction methods, or superstructure distributions, as their rooftop availability for
solar installations may vary substantially. Nonetheless, the methodology presented remains applicable to a broader range of urban contexts if the training dataset is expanded to incorporate diverse rooftop morphologies and obstacle typologies.

Future work should focus on integrating additional data from different geographic areas to improve model generalization and enhance its adaptability to various urban configurations. Furthermore, exploring the impact of adopting newer or more advanced deep learning models for finer segmentation and utilizing high-density LiDAR (Light Detection And Ranging) data could further mitigate data gaps and misalignment. By broadening the dataset's geographic scope and increasing architectural diversity in training, the proposed approach could become a more universally applicable tool for assessing rooftop solar potential in diverse urban settings.

In summary, this research advances the state of the art in assessing the influence of rooftop superstructures on solar potential. The developed workflow is scalable and fully reproducible, and both the trained model and supporting code are publicly available at [46].

## Acknowledgments

This work has been partially supported by MIAI@Grenoble Alpes, (ANR-19-P3IA-0003)

## Data Availability

The data and the model that support the findings of this study are openly available at https://github.com/alessiaboccalatte/swiss_rooftop superstructures.
# Appendix A. Detailed Pixel-Level Semantic Segmentation Metrics

In this appendix, we detail the computation of standard semantic segmentation metrics at the pixel level for each class k (background, rooftop, superstructure). These metrics—Intersection over Union (IoU), Precision, Recall, F1 Score, and Accuracy—are calculated based on the counts of True Positives (TPk), False Positives (FPk), True Negatives (TNk), and False Negatives (FNk) for each class k.

The Intersection over Union (IoU) quantifies the overlap between the predicted and ground truth pixels for class k as shown in Equation A.1:

$$IoU_k = \frac{TP_k}{TP_k + FP_k + FN_k} \qquad (A.1)$$

The Precision for class k measures the proportion of correctly predicted positive pixels among all predicted positive pixels, as defined in Equation A.2:

$$Precision_k = \frac{TP_k}{TP_k + FP_k} \qquad (A.2)$$

The Recall metric evaluates the fraction of actual positive pixels that have been correctly identified, as given in Equation A.3:

$$Recall_k = \frac{TP_k}{TP_k + FN_k} \qquad (A.3)$$

The F1 Score for class k is the harmonic mean of Precision and Recall, providing a balanced measure of both, as expressed in Equation A.4:

$$F1\ Score_k = 2 \times \frac{Precision_k \times Recall_k}{Precision_k + Recall_k} \qquad (A.4)$$

Lastly, the Accuracy across all classes is computed by Equation A.5, which assesses the overall proportion of correctly classified pixels:

$$Accuracy = \frac{\sum_k (TP_k + TN_k)}{\sum_k (TP_k + FP_k + TN_k + FN_k)} \qquad (A.5)$$
References

[1] J. Grözinger, T. Boermans, J. Ashok, F. Wehringer, J. Seehusen, Overview of Member States information on NZEBs Background paper – final report (2014). URL https://energy.ec.europa.eu

[2] T. B. Nadeem, M. Siddiqui, M. Khalid, M. Asif, Distributed energy systems: A review of classification, technologies, applications, and policies:, Energy Strategy Reviews 48 (April) (2023) 101096. doi:10.1016/j.esr.2023.101096.
URL https://doi.org/10.1016/j.esr.2023.101096

[3] IEA, World Energy Outlook 2021 : Part of the World Energy Outlook, International Energy Agency (2021) 386.
URL https://www.iea.org/reports/world-energy-outlook-2021

[4] A. Bartolini, F. Carducci, C. B. Muñoz, G. Comodi, Energy storage and multi energy systems in local energy communities with high renewable energy penetration, Renewable Energy 159 (2020) 595-609. doi:10.1016/j.renene.2020.05.131.

[5] IPCC, Climate Change 2022, Mitigation of Climate Change Summary for Policymakers (SPM), no. 1, 2022.
URL https://www.ipcc.ch/report/ar6/wg2/

[6] G. Desthieux, C. Carneiro, R. Camponovo, P. Ineichen, E. Morello, A. Boulmier, N. Abdennadher, S. Dervey, C. Ellert, Solar energy potential assessment on rooftops and facades in large built environments based on lidar data, image processing, and cloud computing. Methodological background, application, and validation in geneva (solar cadaster), Frontiers in Built Environment 4 (March) (2018). doi:10.3389/fbuil.2018.00014.
[7] A. Boccalatte, M. Thebault, C. Ménézo, J. Ramousse, M. Fossa, Evaluating the impact of urban morphology on rooftop solar radiation: A new city-scale approach based on Geneva GIS data, Energy and Buildings 260 (2022) 111919.
doi:10.1016/j.enbuild.2022.111919.

URL https://doi.org/10.1016/j.enbuild.2022.111919

[8] F. Minelli, I. Ciriello, F. Minichiello, D. D'Agostino, From Net Zero Energy Buildings to an energy sharing model - The role of NZEBs in Renewable Energy Communities, Renewable Energy 223 (February) (2024) 120110.
doi:10.1016/j.renene.2024.120110.

URL https://doi.org/10.1016/j.renene.2024.120110

[9] N. Stendardo, G. Desthieux, N. Abdennadher, P. Gallinelli, GPU-enabled shadow casting for solar potential estimation in large urban areas. Application to the solar cadaster of Greater Geneva, Applied Sciences (Switzerland) 10 (15) (2020).
doi:10.3390/APP10155361.

[10] A. A. A. Gassar, S. H. Cha, Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales, Applied Energy 291 (October 2020) (2021) 116817.
doi:10.1016/j.apenergy.2021.116817.

URL https://doi.org/10.1016/j.apenergy.2021.116817

[11] G. Desthieux, C. Carneiro, A. Susini, N. Abdennadher, A. Boulmier, A. Dubois, R. Camponovo, D. Beni, M. Bach, P. Leverington, E. Morello, Solar cadaster of geneva: A decision support system for sustainable energy management, in: B. Otjacques, P. Hitzelberger, S. Naumann, V. Wohlgemuth (Eds.), From Science to Society, Springer International Publishing, Cham, 2018, pp. 129–137.
[12] J. F. Gaviria, G. Narváez, C. Guillen, L. F. Giraldo, M. Bressan, Machine
learning in photovoltaic systems: A review, Renewable Energy 196 (2022) 298-
318. doi:10.1016/j.renene.2022.06.105.

[13] M. Thebault, V. Clivillé, L. Berrah, G. Desthieux, Multicriteria roof sorting for the integration of photovoltaic systems in urban environments, Sustainable Cities and Society 60 (May) (2020) 102259. doi:10.1016/j.scs.2020.102259.
URL https://doi.org/10.1016/j.scs.2020.102259

[14] S. Freitas, M. Silva, E. Silva, A. Marceddu, M. Miccoli, P. Gnatyuk, L. Marangoni, A. Amicone, An Artificial Intelligence-Based Framework to Accelerate Data-Driven Policies to Promote Solar Photovoltaics in Lisbon, Solar RRL 7 (24) (2023) 1-12. doi:10.1002/solr.202300597.

[15] P. Redweik, C. Catita, M. Brito, Solar energy potential on roofs and facades in an urban landscape, Solar Energy 97 (2013) 332-341. doi:10.1016/j.solener.2013.08.036.

[16] A. Chadzynski, S. Li, A. Grisiute, F. Farazi, C. Lindberg, S. Mosbach, P. Herthogs, M. Kraft, Semantic 3D City Agents—An intelligent automation for dynamic geospatial knowledge graphs, Energy and AI 8 (November 2021) (2022) 100137. doi:10.1016/j.egyai.2022.100137.
URL https://doi.org/10.1016/j.egyai.2022.100137

[17] A. Chadzynski, N. Krdzavac, F. Farazi, M. Q. Lim, S. Li, A. Grisiute, P. Herthogs, A. von Richthofen, S. Cairns, M. Kraft, Semantic 3D City Database — An enabler for a dynamic geospatial knowledge graph, Energy and AI 6 (June) (2021) 100106. doi:10.1016/j.egyai.2021.100106.
URL https://doi.org/10.1016/j.egyai.2021.100106
[18] G. Peronato, S. Bonjour, J. Stoeckli, E. Rey, M. Andersen, Sensitivity of calculated solar irradiation to the level of detail: insights from the simulation of four sample buildings in urban areas, in: PLEA 2016: 32nd International Conference on Passive and Low Energy Architecture, Los Angeles, 2016.

[19] S. Freitas, C. Catita, P. Redweik, M. C. Brito, Modelling solar potential in the urban environment: State-of-the-art review, Renewable and Sustainable Energy Reviews 41 (2015) 915–931. doi:10.1016/j.rser.2014.08.060.
URL http://dx.doi.org/10.1016/j.rser.2014.08.060

[20] N. Jakica, State-of-the-art review of solar design tools and methods for assessing daylighting and solar potential for building-integrated photovoltaics, Renewable and Sustainable Energy Reviews 81 (2018) 1296–1328. doi:10.1016/j.rser.2017.05.080.

[21] B. E. Aissou, A. B. Aissa, A. Dairi, F. Harrou, A. Wichmann, M. Kada, Building Roof Superstructures Classification from Imbalanced and Low Density Airborne LiDAR Point Cloud, IEEE Sensors Journal 21 (13) (2021) 14960–14976. doi:10.1109/JSEN.2021.3073535.

[22] B. G. Pantoja-Rosero, R. Achanta, M. Kozinski, P. Fua, F. Perez-Cruz, K. Beyer, Generating LOD3 building models from structure-from-motion and semantic segmentation, Automation in Construction 141 (July 2021) (2022) 104430. doi:10.1016/j.autcon.2022.104430.
URL https://doi.org/10.1016/j.autcon.2022.104430

[23] S. Izquierdo, M. Rodrigues, N. Fueyo, A method for estimating the geographical distribution of the available roof surface area for large-scale pho-
tovoltaic energy-potential evaluations, Solar Energy 82 (10) (2008) 929-939.
doi:10.1016/j.solener.2008.03.007.

[24] L. K. Wiginton, H. T. Nguyen, J. M. Pearce, Quantifying rooftop solar photovoltaic potential for regional renewable energy policy, Computers, Environment and Urban Systems 34 (4) (2010) 345-357.
doi:10.1016/j.compenvurbsys.2010.01.001.
URL http://dx.doi.org/10.1016/j.compenvurbsys.2010.01.001

[25] J. Schallenberg-Rodríguez, Photovoltaic techno-economical potential on roofs in regions and islands: The case of the Canary Islands. Methodological review and methodology proposal, Renewable and Sustainable Energy Reviews 20 (2013) 219-239. doi:10.1016/j.rser.2012.11.078.

[26] J. Melius, R. Margolis, S. Ong, Estimating Rooftop Suitability for PV : A Review of Methods, Patents, and Validation Techniques, NREL Technical Report (December) (2013) 35.
URL www.nrel.gov/publications

[27] J. Ordóñez, E. Jadraque, J. Alegre, G. Martínez, Analysis of the photovoltaic solar energy capacity of residential rooftops in Andalusia (Spain), Renewable and Sustainable Energy Reviews 14 (7) (2010) 2122-2130.
doi:10.1016/j.rser.2010.01.001.

[28] K. Mainzer, S. Killinger, R. McKenna, W. Fichtner, Assessment of rooftop photovoltaic potentials at the urban level using publicly available geo-data and image recognition techniques, Solar Energy 155 (2017) 561-573.
doi:10.1016/j.solener.2017.06.065.
URL https://doi.org/10.1016/j.solener.2017.06.065
[29] J. Canny, A computational approach to edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-8 (6) (1986) 679-698.
doi:10.1109/TPAMI.1986.4767851.

[30] R. O. Duda, P. E. Hart, Use of the Hough Transformation to Detect Lines and Curves in Pictures, Communications of the ACM 15 (1) (1972) 11-15.
doi:10.1145/361237.361242.

[31] D. Marmanis, K. Schindler, J. D. Wegner, S. Galliani, M. Datcu, U. Stilla, Classification with an edge: Improving semantic image segmentation with boundary detection, ISPRS Journal of Photogrammetry and Remote Sensing 135 (2018) 158-172. arXiv:1612.01337, doi:10.1016/j.isprsjprs.2017.11.009.
URL https://doi.org/10.1016/j.isprsjprs.2017.11.009

[32] K. Bittner, S. Cui, P. Reinartz, Building extraction from remote sensing data using fully convolutional networks, Vol. 42, 2017, p. 481 – 486, cited by: 94; All Open Access, Gold Open Access. doi:10.5194/isprs-archives-XLII-1-W1-481-2017.
URL https://isprs-archives.copernicus.org/articles/XLII-1-W1/481/2017/

[33] J. Yang, B. Matsushita, H. Zhang, Improving building rooftop segmentation accuracy through the optimization of UNet basic elements and image foreground-background balance, ISPRS Journal of Photogrammetry and Remote Sensing 201 (January) (2023) 123-137. doi:10.1016/j.isprsjprs.2023.05.013.
URL https://doi.org/10.1016/j.isprsjprs.2023.05.013

[34] J. Yang, J. Wu, J. Lu, X. Peng, H. Yuan, L. Lei, Lai Affiliations, A novel method for assessment rooftop PV potential based on remote sensing images, Renewable
Energy 237 (PC) (2024) 121810. doi:10.1016/j.renene.2024.121810.

URL https://ssrn.com/abstract=4900382

[35] S. Lee, S. Iyengar, M. Feng, P. Shenoy, S. Maji, Deeproof: A data-driven approach for solar potential estimation using rooop imagery, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) 2105–2113doi:10.1145/3292500.3330741.

[36] S. Krapf, L. Bogenrieder, F. Netzler, G. Balke, M. Lienkamp, RID—Roof Information Dataset for Computer Vision-Based Photovoltaic Potential Assessment, Remote Sensing 14 (10) (2022). doi:10.3390/rs14102299.

[37] R. Pueblas, P. Kuckertz, J. M. Weinand, L. Kotzur, D. Stolten, ETHOS.PASSION: An open-source workflow for rooftop photovoltaic potential assessments from satellite imagery, Solar Energy 265 (October) (2023) 112094. doi:10.1016/j.solener.2023.112094.

URL https://doi.org/10.1016/j.solener.2023.112094

[38] Q. Li, S. Krapf, Y. Shi, X. X. Zhu, SolarNet: A convolutional neural network-based framework for rooftop solar potential estimation from aerial imagery, International Journal of Applied Earth Observation and Geoinformation 116 (November 2022) (2023) 103098. doi:10.1016/j.jag.2022.103098.

URL https://doi.org/10.1016/j.jag.2022.103098

[39] F. Faltermeier, B. Sc, S. Krapf, M. Sc Bruno Willenborg, M. Sc Munich, Improving CNN roof segment detection by dataset extension using 3D city models (April) (2022).

[40] M. C. Bouaziz, M. El Koundi, G. Ennine, High-resolution solar panel detection
in Sfax, Tunisia: A UNet-Based approach, Renewable Energy 235 (August) (2024). doi:10.1016/j.renene.2024.121171.

[41] Z. Guo, Z. Zhuang, H. Tan, Z. Liu, P. Li, Z. Lin, W. L. Shang, H. Zhang, J. Yan, Accurate and generalizable photovoltaic panel segmentation using deep learning for imbalanced datasets, Renewable Energy 219 (P1) (2023) 119471. doi:10.1016/j.renene.2023.119471.
URL https://doi.org/10.1016/j.renene.2023.119471

[42] G. Kasmi, Y. M. Saint-Drenan, D. Trebosc, R. Jolivet, J. Leloux, B. Sarr, L. Dubus, A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata, Scientific Data 10 (1) (2023) 1–13. arXiv:2209.03726, doi:10.1038/s41597-023-01951-4.

[43] R. Castello, S. Roquette, M. Esguerra, A. Guerra, J. L. Scartezzini, Deep learning in the built environment: Automatic detection of rooftop solar panels using Convolutional Neural Networks, Journal of Physics: Conference Series 1343 (1) (2019). doi:10.1088/1742-6596/1343/1/012034.

[44] G. Li, Z. Wang, C. Xu, T. Li, J. Gao, Q. Mao, S. Chen, A district-scale spatial distribution evaluation method of rooftop solar energy potential based on deep learning, Solar Energy 268 (September 2023) (2024) 112282. doi:10.1016/j.solener.2023.112282.
URL https://doi.org/10.1016/j.solener.2023.112282

[45] D. Assouline, N. Mohajeri, J. L. Scartezzini, Large-scale rooftop solar photovoltaic technical potential estimation using Random Forests, Applied Energy 217 (February) (2018) 189–211. doi:10.1016/j.apenergy.2018.02.118.
URL https://doi.org/10.1016/j.apenergy.2018.02.118
[46] A. Boccalatte, swiss_rooftop superstructures, accessed on 10/12/2024 (2024).
URL https://github.com/alessiaboccalatte/swiss_rooftop superstructures

[47] Federal Office of Topography swisstopo, Orthophotos - swisstopo, accessed: 2024-05-23 (2024).
URL https://www.swisstopo.admin.ch/fr/orthophotos

[48] Swiss Federal Statistical Office, opendata.swiss, accessed: 2024-05-23 (2024).
URL https://opendata.swiss/en

[49] SITG, Superstructures des toits des bâtiments, accessed: 2024-11-23 (2019).
URL https://ge.ch/sitg/fiche/1148

[50] A. Ursula, R. Walch, Spatio-Temporal Estimation of Renewable Energy Poten-
tial in Built Environments using Big Data Faculté de l'environnement naturel,
architectural et construit Laboratoire d'énergie solaire et physique du bâtiment
Programme doctoral en énergie (2022).

[51] O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation (2015). arXiv:1505.04597.
URL https://arxiv.org/abs/1505.04597

[52] K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2016-December (2016) 770-778. arXiv:1512.03385, doi:10.1109/CVPR.2016.90.

[53] L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, H. Adam, Encoder-decoder with atrous separable convolution for semantic image segmentation (2018).
[54] J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic segmentation (2014). arXiv:1411.4038.

[55] V. Badrinarayanan, A. Kendall, R. Cipolla, SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (12) (2017) 2481-2495. arXiv:1511.00561, doi:10.1109/TPAMI.2016.2644615.

[56] H. Zhao, J. Shi, X. Qi, X. Wang, J. Jia, Pyramid scene parsing network, Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 2017-January (2017) 6230-6239. arXiv:1612.01105, doi:10.1109/CVPR.2017.660.

[57] B. Recht, R. Roelofs, L. Schmidt, V. Shankar, Do ImageNet classifiers generalize to ImageNet?, 36th International Conference on Machine Learning, ICML 2019 2019-June (2019) 9413-9424. arXiv:1902.10811.

[58] D. P. Kingma, J. Ba, Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980 (2014).

[59] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, Journal of Machine Learning Research 15 (56) (2014) 1929-1958.
URL http://jmlr.org/papers/v15/srivastava14a.html

[60] A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito,
M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, S. Chintala, PyTorch: An Imperative Style, High-Performance Deep Learning Library (2019).

URL https://pytorch.org/

[61] International Energy Agency (IEA), Potential for Building Integrated Photo-voltaics, Technical report, International Energy Agency (IEA) (2002).