# An Explainable Deep Neural Network with Frequency-Aware Channel and Spatial Refinement for Flood Prediction in Sustainable Cities

arXiv: https://arxiv.org/abs/2509.08003 · HTML: https://mlsanity.com/paper/2509.08003/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 76 references that are in the map.
- First posted on arXiv 7 Sep 2025.

## Builds on (most cited first, 30 of 31)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://mlsanity.com/paper/2102.03334/) (2021)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [Coordinate Attention for Efficient Mobile Network Design](https://mlsanity.com/paper/2103.02907/) (2021)
- [Alpha-CLIP: A CLIP Model Focusing on Wherever You Want](https://mlsanity.com/paper/2312.03818/) (2023)
- [MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training](https://mlsanity.com/paper/2311.17049/) (2023)
- [Fast Vision Transformers with HiLo Attention](https://mlsanity.com/paper/2205.13213/) (2022)
- [Improved Zero-Shot Classification by Adapting VLMs with Text Descriptions](https://mlsanity.com/paper/2401.02460/) (2024)
- [Leveraging Vision-Language Models for Improving Domain Generalization in Image Classification](https://mlsanity.com/paper/2310.08255/) (2023)
- [SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classification](https://mlsanity.com/paper/2211.16191/) (2022)
- [CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model](https://mlsanity.com/paper/2305.14014/) (2023)
- [Label Propagation for Zero-shot Classification with Vision-Language Models](https://mlsanity.com/paper/2404.04072/) (2024)
- [Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents](https://mlsanity.com/paper/2201.04236/) (2022)
- [Learning CNN on ViT: A Hybrid Model to Explicitly Class-specific Boundaries for Domain Adaptation](https://mlsanity.com/paper/2403.18360/) (2024)
- [A social context-aware graph-based multimodal attentive learning framework for disaster content classification during emergencies: a benchmark dataset and method](https://mlsanity.com/paper/2410.08814/) (2024)
- [ContCommRTD: A Distributed Content-based Misinformation-aware Community Detection System for Real-Time Disaster Reporting](https://mlsanity.com/paper/2301.12984/) (2023)
- [CoTCoNet: An Optimized Coupled Transformer-Convolutional Network with an Adaptive Graph Reconstruction for Leukemia Detection](https://mlsanity.com/paper/2410.08797/) (2024)
- [An Explainable Contrastive-based Dilated Convolutional Network with Transformer for Pediatric Pneumonia Detection](https://mlsanity.com/paper/2410.16143/) (2024)
- [MNet-SAt: A Multiscale Network with Spatial-enhanced Attention for Segmentation of Polyps in Colonoscopy](https://mlsanity.com/paper/2412.19464/) (2024)
- [A Hybrid Similarity-Aware Graph Neural Network with Transformer for Node Classification](https://mlsanity.com/paper/2504.02615/) (2025)

## Built on by (most cited first, 2 of 2)

- [A multi-temporal multi-spectral attention-augmented deep convolution neural network with contrastive learning for crop yield prediction](https://mlsanity.com/paper/2509.15966/) (2025)
- [Two Stage Context Learning with Large Language Models for Multimodal Stance Detection on Climate Change](https://mlsanity.com/paper/2509.08024/) (2025)

## Abstract (arXiv)

In an era of escalating climate change, urban flooding has emerged as a critical challenge for sustainable cities, threatening lives, infrastructure, and ecosystems. Traditional flood detection methods are constrained by their reliance on unimodal data and static rule-based systems, which fail to capture the dynamic, non-linear relationships inherent in flood events. Furthermore, existing attention mechanisms and ensemble learning approaches exhibit limitations in hierarchical refinement, cross-modal feature integration, and adaptability to noisy or unstructured environments, resulting in suboptimal flood classification performance. To address these challenges, we present XFloodNet, a novel framework that redefines urban flood classification through advanced deep-learning techniques. XFloodNet integrates three novel components: (1) a Hierarchical Cross-Modal Gated Attention mechanism that dynamically aligns visual and textual features, enabling precise multi-granularity interactions and resolving contextual ambiguities; (2) a Heterogeneous Convolutional Adaptive Multi-Scale Attention module, which leverages frequency-enhanced channel attention and frequency-modulated spatial attention to extract and prioritize discriminative flood-related features across spectral and spatial domains; and (3) a Cascading Convolutional Transformer Feature Refinement technique that harmonizes hierarchical features through adaptive scaling and cascading operations, ensuring robust and noise-resistant flood detection. We evaluate our proposed method on three benchmark datasets, such as Chennai Floods, Rhine18 Floods, and Harz17 Floods, XFloodNet achieves state-of-the-art F1-scores of 93.33%, 82.24%, and 88.60%, respectively, surpassing existing methods by significant margins.