Architectural Insights for Post-Tornado Damage Recognition

Robinson Umeike; Thang Dao; Shane Crawford; John van de Lindt; Blythe Johnston; Wanting; Wang; Trung Do

cs.CVFeb 16, 2026

Architectural Insights for Post-Tornado Damage Recognition

Authors: Robinson Umeike, Thang Dao, Shane Crawford, John van de Lindt, Blythe Johnston, Wanting, Wang, Trung Do, +3 more

Organizations: Lisa · The University of Alabama · Colorado State University · Argonne National Laboratory · Old Dominion University · University of South Alabama

Abstract

Rapid and accurate building damage assessment in the immediate aftermath of tornadoes is critical for coordinating life-saving search and rescue operations, optimizing emergency resource allocation, and accelerating community recovery. However, current automated methods struggle with the unique visual complexity of tornado-induced wreckage, primarily due to severe domain shift from standard pre-training datasets and extreme class imbalance in real-world disaster data. To address these challenges, we introduce a systematic experimental framework evaluating 79 open-source deep learning models, encompassing both Convolutional Neural Networks (CNNs) and Vision Transformers, across over 2,300 controlled experiments on our newly curated Quad-State Tornado Damage (QSTD) benchmark dataset. Our findings reveal that achieving operational-grade performance hinges on a complex interaction between architecture and optimization, rather than architectural selection alone. Most strikingly, we demonstrate that optimizer choice can be more consequential than architecture: switching from Adam to SGD provided dramatic F1 gains of +25 to +38 points for Vision Transformer and Swin Transformer families, fundamentally reversing their ranking from bottom-tier to competitive with top-performing CNNs. Furthermore, a low learning rate of 1x10^(-4) proved universally critical, boosting average F1 performance by +10.2 points across all architectures. Our champion model, ConvNeXt-Base trained with these optimized settings, demonstrated strong cross-event generalization on the held-out Tuscaloosa-Moore Tornado Damage (TMTD) dataset, achieving 46.4% Macro F1 (+34.6 points over baseline) and retaining 85.5% Ordinal Top-1 Accuracy despite temporal and sensor domain shifts.

Figures & tables

Element	DS1 - Slight	DS2 - Moderate	DS3 - Extensive	DS4 - Complete
Roof Covering	2–15% of Roof Covering Damaged	15–50% of Roof Covering Damage	More than 50% of Roof Covering Damaged	More than 50% of Roof Covering Damaged (typically)
	AND/OR
Window/Door	1 window or door failure	2 or 3 windows/doors failed	More than 3 windows/doors failed	More than 3 windows/doors failed (typically)
	AND/OR
Roof Sheathing	No Roof Sheathing Failure	1–3 sections of roof sheathing failed	More than 3 sections AND less than 35% of roof sheathing failed	More than 35% of roof sheathing failed
	AND/OR

Table 1: IN-CORE Damage State Classification Criteria for Wood-Frame Residential Structures.

Figure 1: Example of QSTD images for the 6-class IN-CORE taxonomy

Figure 2: (a) Class distribution of the QSTD dataset across training, validation, and test splits, highlighting the pronounced long-tail imbalance. (b) Overall class distribution of the TMTD dataset used for cross-event evaluation

Figure 3: Example of TMTD images for the 6-class IN-CORE taxonomy

Figure 4: The four-stage experimental design, from zero-shot baseline experiment to cross-event generalisation evaluation.

Architectural	Pre-training	Representative Models	Stage 0	Stage 1	Stage 2	Stage 3
Family	Sources	(Stage 0)	(Zero-Shot)	(Base Case)	(Optimization) §	(TTD)
AlexNet	†, $\bigstar$	AlexNet [ 32 ]	2	2	58	✗
ConvNeXt	†	ConvNext-Small [ 44 ]	1	4	116	✓
DenseNet	†, $\bigstar$	DenseNet161 [ 37 ]	2	5	145	✗
EfficientNet	†	EfficientNet-B3 [ 38 ]	1	12	348	✗
GoogLeNet	†	GoogLeNet [ 42 ]	1	1	29	✗

Table 2: Evaluated Architectural Families and Model Variants Across Experimental Stages.

Hyperparameter	Base Case	Search Space	Variations ‡	Domain-Specific Goal
	$(\psi_{\text{base}})$	$(\Psi)$
Activation function	Model Default*	{ReLU, LeakyReLU, GELU, SiLU, ELU}	5	Investigate impact of non-linearity on learning complex damage textures and preventing dying neurons
Batch Size	64	{64, 32, 16, 8, 4}	4	Balance gradient stability with noise-induced regularization; assess impact on rare class representation
Optimizer	Adam	{Adam, SGD (momentum=0.9)}	1	Compare adaptive vs. momentum-based optimization for disaster imagery
Regularization	None	{None, Dropout with $p\in\{0.2,0.3,0.5\}$ }	3	Combat overfitting to dataset-specific debris patterns and lighting artifacts
Input resolution	$224\times 224$	{ $160^{2}$ , $192^{2}$ , $224^{2}$ , $256^{2}$ , $384^{2}$ , $448^{2}$ , $512^{2}$ } †	6	Enable multi-scale feature recognition from fine-grained (windows) to gross (structural) damage

Table 3: Comprehensive Hyperparameter Search Space and Optimization Strategy.

Rank ‡	Model	Pre-training	Macro-F1	Ordinal Top-1	Standard ¶ Top-1	Param ‖	Inference
			(%)	Acc. (%)	Acc. (%)	(M)	Time (ms)
1	EfficientNet-B3	†	14.3	50.5	26.1	10.7	13.3
2	DenseNet161	$\bigstar$	12.6	75.9	61.0	26.5	21.5
3	AlexNet	$\bigstar$	11.3	33.5	23.2	57.0	0.6
4	GoogLeNet	†	8.4	14.8	9.4	5.6	7.1
5	AlexNet	†	6.9	39.5	8.3	57.0	0.6

Table 4: Zero-Shot Transfer Performance of Representative Models on QSTD Test Set.

Figure 5: (a) Zero-Shot Transfer Performance by Pre-training Source. (b) Scatter Plot of Macro F1 vs. Parameters for all 21 zero-shot models showing no correlation between parameter count and F1.

Figure 6: Ordinal confusion matrices for the top three zero-shot models on the QSTD test set. Color indicates ordinal distance (Dark Green = correct, Green = $\pm 1$ , Light Green = $\pm 2$ , Yellow = $\pm 3$ , Orange = $\pm 4$ , Red = $\pm 5$ ). (a) EfficientNet-B3 (ImageNet) shows scattered predictions. (b) AlexNet (Places365) overpredicts DS0 and DS5. (c) DenseNet161 (Places365) collapses to DS0-Undamaged

Figure 7: Ordinal confusion matrices of top-performing Stage 1 models for top three architecture families on the QSTD test set

Figure 8: Scatter plot of Macro F1 vs. Inference Time for all 79 fine-tuned models showing the Pareto frontier

Figure 9: Performance and Efficiency of Top Variants per Family Across Key Experimental Stages. Each subplot shows the best-performing variant from each architectural family for a specific configuration: (a) Stage 1 Base Case ( $\psi_{base}$ ), (b) Stage 2 Optimal Loss Function, (c) Stage 2 Optimal Input Resolution, and (d) Stage 2 Optimal Data Augmentation. (Top Row) Performance comparison using Macro F1 (blue), Ordinal Top-1 Acc (green), and Standard Top-1 Acc (red). (Bottom Row) Corresponding efficiency metrics: Parameter count (purple) and Inference Time (orange)

Figure 10: Hyperparameter Sensitivity Analysis Across Architectures. Box plots show the distribution of Macro F1 scores for all 79 fine-tuned models under varying configurations for nine key hyperparameters (*Base Case configuration): (Top row, L-R) Loss Function, Input Resolution, Data Augmentation; (Middle row, L-R) Optimizer, Learning Rate, LR Scheduler; (Bottom row, L-R) Dropout Regularization, Batch Size, Activation Function.

Figure 11: Effect of Input Resolution on Model Performance and Cost. (a) Average Macro F1 score across 73 convolutional model families. (b) Average computational cost (GFLOPs) and inference time (ms), showing a sharp increase at higher resolutions. (c) Performance scaling for representative top-5 performing models, highlighting architecture-specific peaks

Figure 12: Performance and Efficiency of Top Variants per Family Across Optimizer and Learning Rate. Each subplot shows the best-performing variant from each architectural family for a specific configuration

Figure 13: Performance and Efficiency of Top Variants per Family Across Scheduler, Batch Size, Dropout, and Activation Function. Each subplot shows the best-performing variant from each architectural family for a specific configuration

Figure 14: Pre-training Source Comparison: Average Macro F1 Across Stage 2 Optimizations (Places365 vs. ImageNet-1K)

Model Configuration	Macro F1	Ordinal	Std.	DS0	DS1	DS2	DS3	DS4	DS5
	(%)	Top-1 Acc (%)	Top-1 Acc (%)	(F1)	(F1)	(F1)	(F1)	(F1)	(F1)
Naive Baseline	11.8	43.9	14.0	9.3	0.7	19.4	17.8	7.9	15.9
(ImageNet $\rightarrow$ TMTD)	11.8	43.9	14.0	9.3	0.7	19.4	17.8	7.9	15.9
QSTD-Optimized	46.4	85.5	58.3	63.5	20.6	22.6	36.2	75.5	59.1
(ImageNet $\rightarrow$ QSTD $\rightarrow$ TMTD)	46.4	85.5	58.3	63.5	20.6	22.6	36.2	75.5	59.1

Table 5: Zero-Shot Generalization Performance on the TMTD Dataset (ConvNeXt-Base).

Figure 15: Ordinal Confusion Matrices for Zero-Shot TMTD Test. (a) Naïve baseline model showing a poor classification performance. (b) QSTD-optimized model showing generalizable ordinal understanding of damage severity on an unseen dataset

Component	Recommendation	Impact	Notes
Optimizer	SGD with momentum $=$ 0.9 for Vision Transformers; Adam acceptable for CNNs	+25 to +38 F1 points for ViT/Swin families	Most impactful optimization; elevates Transformers from bottom-tier to competitive performance
Learning Rate	$1\times 10^{-4}$ (universal across all families)	+10.2 F1 points on average	Rates $\geq 1\times 10^{-3}$ consistently resulted in poorer performance
Input Resolution	$256\times 256$ to $448\times 448$	+14.2% F1 for DS1–DS3	Higher resolutions show diminishing returns with increased cost
Data Augmentation	Standard (moderate geometric transforms)	+2.4 F1 vs. none; $-7.2$ F1 with heavy augmentation	Aggressive augmentation degrades fine-grained damage discrimination
Loss Function	Architecture-dependent tuning	Mixed effects (48% improved; 49% degraded with Focal Loss)	Cross-entropy remains the most robust baseline on the QSTD benchmark
Pre-training Source	ImageNet (default); Places365 for AlexNet, DenseNet161, ResNet18	Up to +142% F1 (zero-shot)	Scene-centric pre-training benefits certain architectures

Table 6: Practical Optimization Guidelines based on QSTD Benchmark (Applicable Across All 79 Architectures).

Deployment	Recommended	Optimizer	Learning	Input	Aug-	Performance	Key
Scenario	Model		Rate	Resolution	mentation	Metrics	Advantages
Resource-Constrained (Edge devices, mobile platforms)	MobileNetV3-Large or ShuffleNetV2-x1.5	SGD (momentum=0.9)	$1\times 10^{-4}$	$256\times 256$	Standard	62.0% F1, 4.2M params, $\sim$ 5ms inference	Optimal accuracy-efficiency trade-off for limited compute
Maximum Accuracy (GPU-enabled backend)	ConvNeXt-Base	SGD (momentum=0.9)	$1\times 10^{-4}$	$384\times 384$	Standard	68.0% F1 (QSTD), 46.4% F1 (TMTD), 85.5% Ordinal Acc (TMTD)	Highest accuracy with strong cross-event generalization

Table 7: QSTD-Based Deployment Configurations for Automated Tornado Damage Assessment.

Appendix figures & tables19 assets

Supplementary material from the paper’s appendix.

Appendix

Class	Initial	Final	Net Change	Primary Adjustment
	Annotation	Dataset
DS0 - Undamaged	3,744	3,375	$-369$ ( $-9.9\%$ )	Reclassified to DS1 upon detection of subtle roof/window damage
DS1 - Slight	480	606	$+126$ ( $+26.3\%$ )	Received reclassifications from DS0 and DS2
DS2 - Moderate	512	432	$-80$ ( $-15.6\%$ )	Many reclassified to DS3 based on roof sheathing criteria
DS3 - Extensive	127	368	$+241$ ( $+189.8\%$ )	Significant increase due to consistent application of sheathing failure thresholds
DS4 - Complete	440	455	$+15$ ( $+3.4\%$ )	Minor adjustments; clearest damage state with least ambiguity

Appendix

Table A1: Class Distribution Changes During Annotation and Cross-Validation.

Figure A1: QSTD Dataset Evolution and Refinement through Inter-Annotator Cross-Validation

Damage Class	Average Width (px)	Average Height (px)
DS0 - Undamaged	347.02	225.70
DS1 - Slight	371.34	238.19
DS2 - Moderate	367.34	229.52
DS3 - Extensive	366.38	235.88
DS4 - Complete	373.15	248.80
DS5 - Debris	1048.47	632.23

Appendix

Table A2: QSTD Image Dimensions by Damage Class. The table lists the mean width and height in pixels for images within each of the six damage state categories after initial cropping.

Figure A2: Figure showing the distribution of QSTD image resolutions (Height x Width) for each of the 6 damage classes, highlighting the different characteristics of the DS5 class

Strategy	Transformations	Key Parameters	Context
None	–	–	Provides a non-augmented baseline; used for validation and test sets.
Basic	Horizontal flip	$p=0.3$	Basic invariance to viewpoint.
(Base Case)	Resize	Target input resolution
Standard	Horizontal flip	$p=0.5$	Simulates moderate changes in camera angle and framing
	Random rotation	$\theta\sim\mathcal{U}(-15°,15°)$
	Random crop	From padded input

Appendix

Table B1: Comprehensive List of Data Augmentation Strategies Used in Stage 1 and 2.

#	Architecture	Number of	Models
		Variants
1	AlexNet	2	AlexNet, AlexNet-Places365
2	ConvNeXt	4	ConvNeXt-Tiny, ConvNeXt-Small, ConvNeXt-Base, ConvNeXt-Large
3	DenseNet	5	DenseNet121, DenseNet161, DenseNet161-Places365, DenseNet169, DenseNet201
4	EfficientNet	12	EfficientNet-B0, EfficientNet-B1, EfficientNet-B1v2*, EfficientNet-B2, EfficientNet-B3, EfficientNet-B4, EfficientNet-B5, EfficientNet-B6, EfficientNet-B7, EfficientNet-v2-s, EfficientNet-v2-m, EfficientNet-v2-l
5	GoogLeNet	1	GoogLeNet

Appendix

Table B2: Comprehensive List of All 79 Model Variants.

Rank	Model	Pre-training	Macro F1	Ordinal Top-1	Standard Top-1	Params	Inference
			(Val) (%)	Acc (Val) (%)	Acc (Val) (%)	(M)	Time (ms)
1	EfficientNet-B6	†	66.0	91.5	77.7	40.75	22.89
2	EfficientNet-B3	†	64.4	88.7	77.0	10.71	13.29
3	EfficientNet-B4	†	63.9	89.8	77.4	17.56	16.65
4	EfficientNet-B1v2	†	63.4	89.1	75.5	6.52	11.70
5	ConvNeXt-Large	†	62.8	90.4	75.2	196.21	9.34

Appendix

Table C1: Complete Performance Results for All 79 Model Variants Trained Using the

\psi_{\text{base}}

Configuration.

Metrics (Average)	CCE*	Focal Loss	$\Delta$
Macro F1	52.9	52.4	$-0.5$
Standard Top-1	71.7	71.8	0.0
Ordinal Top-1	86.2	86.1	$-0.1$
Params (M)	58.77	61.54	$+2.77$
GFLOPs	8.98	9.85	$+0.86$
Inference Time (ms)	11.16	11.42	$+0.26$

Appendix

Table C2: Average Performance for Loss Function: CCE vs. Focal Loss (

\gamma=5

\alpha=0.5

Architectural Family	Variants	Variants	Avg. F1 Change	Top F1	Top F1
	Tested	Improved (%)	(Points)	Gain	Loss
AlexNet	2	0 (0)	$-6.70$	—	$-13.4$ (AlexNet-Places365)
ConvNeXt	4	0 (0)	$-10.20$	—	$-17.7$ (ConvNeXt-Base)
DenseNet	5	2 (40)	$-1.70$	$+1.6$ (DenseNet161)	$-6.0$ (DenseNet121)
EfficientNet	12	6 (50)	$+0.50$	$+5.6$ (EfficientNet-B2)	$-3.4$ (EfficientNet-B6)
GoogLeNet	1	0 (0)	$-0.30$	—	$-0.3$ (GoogLeNet)

Appendix

Table C3: Per-Family Impact of Focal Loss (

\gamma=5

\alpha=0.5

) vs. CCE Baseline.

Metrics (Average)	224*	160	192	256	384	448	512
Macro F1	52.9%	53.0%	52.7%	52.8%	53.5%	53.9%	52.7%
Standard Top-1	71.7%	72.1%	71.8%	72.4%	72.4%	72.3%	72.1%
Ordinal Top-1	86.2%	86.4%	86.4%	86.5%	86.5%	86.9%	86.1%
Params (M)	58.77	52.57	52.57	52.57	52.57	52.57	52.57
GFLOPs	8.98	4.17	6.00	10.66	23.96	32.61	42.59
Inference Time (ms)	11.16	11.11	11.19	11.27	11.46	11.66	11.88

Appendix

Table C4: Average Macro F1 and Efficiency Metrics Across All Models for Input Resolution.

Figure C1: Figure showing the performance progression of QSTD image resolutions (Height x Width) for each of the 6 damage classes.

Figure C2: Heatmap of Per-Class F1 Score Percentage Change by Input Resolution, Relative to the $224^{2}$ Baseline. The blue-bounded region ( $256^{2}$ – $448^{2}$ ) highlights the optimal resolution range, where the most significant performance gains (+14.2 for DS1) are concentrated in the challenging intermediate damage states (DS1-DS3).

Augmentation Strategy	None	Basic*	Standard	Advanced	Heavy
Avg. Macro F1	51.6%	52.9%	54.0%	50.2%	46.8%
Avg. Standard Top-1	71.3%	71.7%	72.5%	71.9%	70.7%
Avg. Ordinal Top-1	85.7%	86.2%	86.4%	85.8%	84.6%
Avg. Params (M)	58.78	58.77	58.77	58.78	61.54
Avg. GFLOPs	8.98	8.98	8.98	8.98	9.85
Avg. Inference Time (ms)	11.23	11.16	11.23	11.21	11.43

Appendix

Table C5: Average Macro F1 and Efficiency Metrics Across All Models for Data Augmentation Strategies.

Metrics (Average)	Adam*	SGD	$\Delta$
Macro F1	52.9%	59.8%	$+6.9\%$
Standard Top-1	71.7%	74.6%	$+2.9\%$
Ordinal Top-1	86.2%	89.3%	$+3.2\%$
Params (M)	58.77	61.28	$+2.51$
GFLOPs	8.98	9.85	$+0.86$
Inference Time (ms)	11.16	11.42	$+0.26$

Appendix

Table C6: Average Performance for Optimizer (Adam vs. SGD).

Metrics (Average)	$1\times 10^{-3}$ *	$1\times 10^{-4}$	$3\times 10^{-3}$	$5\times 10^{-3}$
Macro F1	52.9%	63.1%	43.6%	40.9%
Standard Top-1	71.7%	76.2%	69.1%	68.3%
Ordinal Top-1	86.2%	90.3%	83.8%	82.5%
Params (M)	58.77	61.28	61.28	61.28
GFLOPs	8.98	9.85	9.85	9.85
Inference Time (ms)	11.16	11.43	11.40	11.41

Appendix

Table C7: Average Performance for Learning Rate Configurations.

Metrics (Average)	None*	Cosine	Step
Macro F1	52.9%	52.8%	53.8%
Standard Top-1	71.7%	71.8%	73.2%
Ordinal Top-1	86.2%	86.0%	86.9%
Params (M)	58.77	61.53	61.53
GFLOPs	8.98	9.85	9.85
Inference Time (ms)	11.16	11.46	11.39

Appendix

Table C8: Average Performance for LR Scheduler Strategies.

Metrics (Average)	64*	32	16	8	4
Macro F1	52.9%	51.0%	48.5%	45.7%	43.3%
Standard Top-1	71.7%	71.4%	70.8%	70.1%	69.2%
Ordinal Top-1	86.2%	85.4%	84.8%	84.1%	82.8%
Params (M)	58.77	61.28	61.28	61.28	61.28
GFLOPs	8.98	9.85	9.85	9.85	9.85
Inference Time (ms)	11.16	11.40	11.37	11.41	11.37

Appendix

Table C9: Average Performance for Batch Size Configurations.

Metrics (Average)	0.0*	0.2	0.3	0.5
Macro F1	52.9%	52.5%	52.7%	52.1%
Standard Top-1	71.7%	71.8%	71.8%	71.4%
Ordinal Top-1	86.2%	86.2%	86.2%	85.8%
Params (M)	58.77	61.28	61.28	61.28
GFLOPs	8.98	9.85	9.85	9.85
Inference Time (ms)	11.16	11.71	11.69	11.63

Appendix

Table C10: Average Performance for Dropout Rates.

Metrics (Average)	Default*	ReLU	Leaky ReLU	ELU	SiLU	GELU
Macro F1	52.9%	50.5%	49.6%	44.5%	46.5%	48.7%
Standard Top-1	71.7%	70.5%	70.3%	69.2%	69.8%	70.5%
Ordinal Top-1	86.2%	85.2%	84.5%	83.2%	83.8%	84.4%
Params (M)	58.77	61.28	61.28	61.28	61.28	61.28
GFLOPs	8.98	9.85	9.85	9.85	9.85	9.85
Inference Time (ms)	11.16	11.60	11.60	11.61	11.52	11.57

Appendix

Table C11: Average Performance for Activation Function Configurations.

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TornadoNet: Real-Time Building Damage Detection with Ordinal Supervision

Robinson Umeike, Cuong Pham, Ryan Hausen, Thang Dao+7

The University of Alabama · University of South Alabama · Johns Hopkins University+2

Disaster Damage Assessment Building Damage Assessment Real-Time Object Detection Ordinal Regression Detection Transformer

Open paper AskPDF arXiv

We present TornadoNet, a comprehensive benchmark for automated street-level building damage assessment evaluating how modern real-time object detection architectures and ordinal-aware supervision strategies perform under realistic post-disaster conditions. TornadoNet provides the first controlled benchmark demonstrating how architectural design and loss formulation jointly influence multi-level damage detection from street-view imagery, delivering methodological insights and deployable tools for disaster response. Using 3,333 high-resolution geotagged images and 8,890 annotated building instances from the 2021 Midwest tornado outbreak, we systematically compare CNN-based detectors from the YOLO family against transformer-based models (RT-DETR) for multi-level damage detection. Models are trained under standardized protocols using a five-level damage classification framework based on IN-CORE damage states, validated through expert cross-annotation. Baseline experiments reveal complementary architectural strengths. CNN-based YOLO models achieve highest detection accuracy and throughput, with larger variants reaching 46.05% [email protected] at 66-276 FPS on A100 GPUs. Transformer-based RT-DETR models exhibit stronger ordinal consistency, achieving 88.13% Ordinal Top-1 Accuracy and MAOE of 0.65, indicating more reliable severity grading despite lower baseline mAP. To align supervision with the ordered nature of damage severity, we introduce soft ordinal classification targets and evaluate explicit ordinal-distance penalties. RT-DETR trained with calibrated ordinal supervision achieves 44.70% [email protected], a 4.8 percentage-point improvement, with gains in ordinal metrics (91.15% Ordinal Top-1 Accuracy, MAOE = 0.56). These findings establish that ordinal-aware supervision improves damage severity estimation when aligned with detector architecture. Model & Data: https://github.com/crumeike/TornadoNet

Abstract

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