Water Body Extraction Research Articles

Small water body extraction in remote sensing with enhanced CNN architecture

In climate change, the precise acquisition of data about natural resources is essential and poses a significant difficulty in remote sensing. Identifying water resources has become a key focus in addressing the global issue of water scarcity. Using remote sensing techniques to extract water bodies from satellite images is complex. While Convolutional Neural Networks (CNNs) have shown potential in image segmentation, challenges such as unclear boundaries, the requirement for extensive training data, and a high number of trainable parameters persist. To address these challenges, a CNN architecture called ”WaterNet” has been developed by integrating two residual refinement capsules to improve predicted results and ensure continuity in boundary pixels. Incorporating dense layers within the network reduces the total count of trainable parameters, thus alleviating the computational burden. To assess the effectiveness of the proposed method, a comparison was made with several conventional and deep learning approaches. These included the use of NDWI, Unet, Deepunet, Segnet, DeepwaterMap, and Multi-layered Feature Fusion Approach (MFFA). The proposed solution demonstrated superior performance, surpassing all existing approaches, with an outstanding accuracy of 0.97% in extracting water resources from high-resolution satellite imagery. The model is accurate in distinguishing water from other features such as land, ice, clouds, and shadows using sentinel data as input. The uniqueness of this work lies in the development of the ”WaterNet” system tailored for water body extraction, showcasing improved accuracy, efficient context transmission, incorporation of dense layers, utilization of residual refinement modules, and addressing the limitations of conventional approaches. These contributions collectively advance remote sensing and deep learning used in the detection of small bodies of water from imagery captured by satellites.

Agricultural surface water extraction in environmental remote sensing: A novel semantic segmentation model emphasizing contextual information enhancement and foreground detail attention

The intelligent and efficient analysis of agricultural surface water is essential for agricultural irrigation, flood prevention, and resource utilization. However, accurate extraction from high-resolution optical remote sensing images is challenging due to agricultural heterogeneous terrains, dynamic water body scales, and diverse surrounding vegetation. To address these challenges, this paper introduces a novel architecture called the Complex Rural Water Extraction Network (CRWENet). The CRWENet is designed to enhance the understanding of contextual information and suppress random background noise, which comprises four key components including feature encoder, feature decoder, context information enhancement module, and foreground detail attention module. The context information enhancement module harnesses the long-range dependency-capturing capabilities of Transformer, merging multi-scale features to enhance global information perception. Meanwhile, the foreground detail attention module facilitates adaptive suppression of intricate background noise and accurate delineation of agricultural surface water boundaries by harmonizing interactions across channel, height, and width dimensions. Extensive experimentation on the LoveDA dataset and the GLH-water dataset validates the effectiveness of CRWENet. The results demonstrate that the proposed CRWENet outperforms the existing classical semantic segmentation networks and the mainstream water body extraction methods, achieving Intersection over Union (IoU) scores of 70.93% and 80.55% respectively.

Improving Flood Streamflow Estimation of Ungauged Small Reservoir Basins Using Remote Sensing and Hydrological Modeling

Small- and medium-sized reservoirs significantly alter natural flood processes, making it essential to understand their impact on runoff for effective water resource management. However, the lack of measured data for most small reservoirs poses challenges for accurately simulating their behavior. This study proposes a novel method that utilizes readily available satellite observation data, integrating hydraulic, hydrological, and mathematical formulas to derive outflow coefficients. Based on the Grid-XinAnJiang (GXAJ) model, the enhanced GXAJ-R model accounts for the storage and release effects of ungauged reservoirs and is applied to the Tunxi watershed. Results show that the original GXAJ model achieved a stable performance with an average NSE of 0.88 during calibration, while the NSE values of the GXAJ and GXAJ-R models during validation ranged from 0.78 to 0.97 and 0.85 to 0.99, respectively, with an average improvement of 0.03 in the GXAJ-R model. This enhanced model significantly improves peak flow simulation accuracy, reduces relative flood peak error by approximately 10%, and replicates the flood flow process with higher fidelity. Additionally, the area–volume model derived from classified small-scale data demonstrates high accuracy and reliability, with correlation coefficients above 0.8, making it applicable to other ungauged reservoirs. The OTSU-NDWI method, which improves the NDWI, effectively enhances the accuracy of water body extraction from remote sensing, achieving overall accuracy and kappa coefficient values exceeding 0.8 and 0.6, respectively. This study highlights the potential of integrating satellite data with hydrological models to enhance the understanding of reservoir behavior in data-scarce regions. It also suggests the possibility of broader applications in similarly ungauged basins, providing valuable tools for flood management and risk assessment.

EDWNet: A Novel Encoder–Decoder Architecture Network for Water Body Extraction from Optical Images

Automated water body (WB) extraction is one of the hot research topics in the field of remote sensing image processing. To address the challenges of over-extraction and incomplete extraction in complex water scenes, we propose an encoder–decoder architecture semantic segmentation network for high-precision extraction of WBs called EDWNet. We integrate the Cross-layer Feature Fusion (CFF) module to solve difficulties in segmentation of WB edges, utilizing the Global Attention Mechanism (GAM) module to reduce information diffusion, and combining with the Deep Attention Module (DAM) module to enhance the model’s global perception ability and refine WB features. Additionally, an auxiliary head is incorporated to optimize the model’s learning process. In addition, we analyze the feature importance of bands 2 to 7 in Landsat 8 OLI images, constructing a band combination (RGB 763) suitable for algorithm’s WB extraction. When we compare EDWNet with various other semantic segmentation networks, the results on the test dataset show that EDWNet has the highest accuracy. EDWNet is applied to accurately extract WBs in the Weihe River basin from 2013 to 2021, and we quantitatively analyzed the area changes of the WBs during this period and their causes. The results show that EDWNet is suitable for WB extraction in complex scenes and demonstrates great potential in long time-series and large-scale WB extraction.

WaterGPT: Training a Large Language Model to Become a Hydrology Expert

This paper introduces WaterGPT, a language model designed for complex multimodal tasks in hydrology. WaterGPT is applied in three main areas: (1) processing and analyzing data such as images and text in water resources, (2) supporting intelligent decision-making for hydrological tasks, and (3) enabling interdisciplinary information integration and knowledge-based Q&A. The model has achieved promising results. One core aspect of WaterGPT involves the meticulous segmentation of training data for the supervised fine-tuning phase, sourced from real-world data and annotated with high quality using both manual methods and GPT-series model annotations. These data are carefully categorized into four types: knowledge-based, task-oriented, negative samples, and multi-turn dialogues. Additionally, another key component is the development of a multi-agent framework called Water_Agent, which enables WaterGPT to intelligently invoke various tools to solve complex tasks in the field of water resources. This framework handles multimodal data, including text and images, allowing for deep understanding and analysis of complex hydrological environments. Based on this framework, WaterGPT has achieved over a 90% success rate in tasks such as object detection and waterbody extraction. For the waterbody extraction task, using Dice and mIoU metrics, WaterGPT’s performance on high-resolution images from 2013 to 2022 has remained stable, with accuracy exceeding 90%. Moreover, we have constructed a high-quality water resources evaluation dataset, EvalWater, which covers 21 categories and approximately 10,000 questions. Using this dataset, WaterGPT achieved the highest accuracy to date in the field of water resources, reaching 83.09%, which is about 17.83 points higher than GPT-4.

Comparative Analysis of Water Body Extraction Accuracy Based on Thresholding Method

Abstract. With the continuous development of remote sensing technology, various methods for land water body extraction based on satellite remote sensing have emerged. The thresholding method, as a commonly used image segmentation technique, possesses advantages such as high efficiency and wide applicability, making it widely employed in water body extraction research. In this thesis, utilizing SPOT4 imagery, we conducted experimental comparisons of water body extraction using the Iteration thresholding algorithm, Kittler-Illingworth (KI) thresholding algorithm, and Otsu thresholding algorithm. The experimental results demonstrate that all three thresholding methods exhibit good performance in water body extraction, with the Kittler-Illingworth (KI) thresholding algorithm achieving the highest relative accuracy.

Satellite-based Spectral Indices for Extracting Village Water Bodies in Ludhiana District of Punjab, India

Ensuring water security is the foremost priority of every nation, and water bodies are the most precious life-sustaining resources on the earth. Availability of reliable and updated information of water bodies including knowledge about their locations is necessary for the sustainable planning and management of water resources at a regional scale. Remote sensing technique has revolutionized extraction of village water bodies as it provides fine spatial resolution, gives rapid and precise results and covers large areas, and hence, it has been widely used in the recent studies. In this study, three remote sensing-based spectral indices, i.e., normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and normalized difference pond index (NDPI) were employed to demarcate the area of ponds in each village of Ludhiana district in Punjab. Accuracy of the spectral indices was evaluated by comparing the area of the ponds delineated by the indices with that of digitized manually. The results indicated that NDWI, MNDWI and NDPI could identify 370, 1263 and 1410 number of village ponds, respectively. On the other hand, a total of 1513 village ponds could be demarcated through manual digitization. Thus, the efficiency of NDWI in identification of correct number of village ponds was 24.5%, while the efficiency of MNDWI and NDPI was 83.5% and 93.2%, respectively. Furthermore, NDPI demonstrated an efficiency of 60-65% in demarcating the area of village ponds.

Improving the UNet with channel attention mechanism for Taihu Lake water body recognition

Abstract Accurate recognition of surface water bodies is important for disaster management and resource planning. To explore a higher-precision water body recognition method, propose an improved channel attention mechanism UNet (ECA-UNet) water body recognition model based on Sentinel-2A remote sensing images. Utilizing ECA-UNet, UNet, PSPnet, and DeeplabV3, as well as the traditional normalized water body index (NDWI), recognize the water body in the Taihu Lake area. Through comparative analysis, the following main conclusions are drawn: (1) The recognition accuracy of the deep learning network model is better than the traditional NDWI method, among which the ECA-UNet network model has the highest accuracy of 98.67%, and the lowest recognition accuracy of NDWI is 89.06%. (2) The ECA-UNet network model can effectively extract water body recognition targets, has a better recognition effect on local features of water bodies, and can improve the water body extraction effect. The above results show that the improved channel attention mechanism ECA-UNet has the highest accuracy in water body recognition in Taihu Lake, which provides a reference for subsequent research and applications in this field.

Automatic Water Body Extraction from SAR Images Based on MADF-Net

Water extraction from synthetic aperture radar (SAR) images has an important application value in wetland monitoring, flood monitoring, etc. However, it still faces the problems of low generalization, weak extraction ability of detailed information, and weak suppression of background noises. Therefore, a new framework, Multi-scale Attention Detailed Feature fusion Network (MADF-Net), is proposed in this paper. It comprises an encoder and a decoder. In the encoder, ResNet101 is used as a solid backbone network to capture four feature levels at different depths, and then the proposed Deep Pyramid Pool (DAPP) module is used to perform multi-scale pooling operations, which ensure that key water features can be captured even in complex backgrounds. In the decoder, a Channel Spatial Attention Module (CSAM) is proposed, which focuses on feature areas that are critical for the identification of water edges by fusing attention weights in channel and spatial dimensions. Finally, the high-level semantic information is effectively fused with the low-level edge features to achieve the final water detection results. In the experiment, Sentinel-1 SAR images of three scenes with different characteristics and scales of water body are used. The PA and IoU of water extraction by MADF-Net can reach 92.77% and 89.03%, respectively, which obviously outperform several other networks. MADF-Net carries out water extraction with high precision from SAR images with different backgrounds, which could also be used for the segmentation and classification of other tasks from SAR images.

Unlocking the potential of CYGNSS for pan-tropical inland water mapping through multi-source data and transformer

Cyclone Global Navigation Satellite System (CyGNSS) data are widely recognized for their sensitivity to inland water bodies. However, the detection of water bodies using single CyGNSS data is subject to uncertainties, presenting challenges for large-scale and accurate water system detection. In this study, we employ CyGNSS data for regression estimation to map inland water bodies. In comparison to previous studies, we incorporate additional constraints, including topographic factors, vegetation information, soil moisture, and latitude and longitude data. Leveraging the U-shaped structure, Swin Transformer, and ContextModule, we effectively extract water body distribution information, referred to as CFRT. Through rigorous performance comparison with prevalent deep learning models, our method demonstrates remarkable accuracy. The generated water percent exhibits notable consistency with the reference data, achieving a root mean square error (RMSE) of 7.15% and a mean intersection over union of 0.778 within the reachable area of the CyGNSS data. Our approach emphasizes the significance of utilizing multi-source data to substantially enhance the accuracy of CyGNSS water system detection.

Extraction of River Water Bodies Based on ICESat-2 Photon Classification

The accurate extraction of river water bodies is crucial for the utilization of water resources and understanding climate patterns. Compared with traditional methods of extracting rivers using remote sensing imagery, the launch of satellite-based photon-counting LiDAR (ICESat-2) provides a novel approach for river water body extraction. The use of ICESat-2 ATL03 photon data for inland river water body extraction is relatively underexplored and thus warrants investigation. To extract inland river water bodies accurately, this study proposes a method based on the spatial distribution of ATL03 photon data and the elevation variation characteristics of inland river water bodies. The proposed method first applies low-pass filtering to denoised photon data to mitigate the impact of high-frequency signals on data processing. Then, the elevation’s standard deviation of the low-pass-filtered data is calculated via a sliding window, and the photon data are classified on the basis of the standard deviation threshold obtained through Gaussian kernel density estimation. The results revealed that the average overall accuracy (OA) and Kappa coefficient (KC) for the extraction of inland river water bodies across the four study areas were 99.12% and 97.81%, respectively. Compared with the improved RANSAC algorithm and the combined RANSAC and DBSCAN algorithms, the average OA of the proposed method improved by 17.98% and 7.12%, respectively, and the average KC improved by 58.38% and 17.69%, respectively. This study provides a new method for extracting inland river water bodies.

WATER BODIES EXTRACTION USING MATHEMATICAL MORPHOLOGY

The management of water resources is vital for maintaining the world’s ecosystems. Conventional methods of extracting water bodies remain very limited due to the complexity of the implementation. This leads to a reduction in the extraction precision. Our main objec- tive is to improve the detection of water bodies. We tested the accuracy of our method on the Sentinel-2 Dataset that contains images with different complexity levels and heterogeneous structures like shadows, roads, buildings, etc. This article presents an original method that implements the idea of separating the three-component RGB image matrices and then processing only the green matrix because it contains all water bodies with high precision. Our method is based mainly on the mathematical morphology. Firstly, we propose a simple and fast binary algorithm to detect the maximum of water bodies existing in the images. This step was carried out using the Hit-or-Miss Transform. The second step exploits applying the Top-Hat Transform to refine the segmentation result. By comparing our method with several currently used methods, we notice that our method improves the quality of segmentation and gives excellent results, which exceed 95% for all the metrics used to calculate the classification quality in the purview of remote sensing. The error obtained with our method remains less than 1 %. We can affirm that our method is very suitable for detecting bodies of water compared to all current methods.

A New and Robust Index for Water Body Extraction from Sentinel-2 Imagery

Land surface water is a key part in the global ecosystem balance and hydrological cycle. Remote sensing has become an effective tool for its spatio-temporal monitoring. However, remote sensing results exemplified in so-called water indices are subject to several limitations. This paper proposes a new and effective water index called the Sentinel Multi-Band Water Index (SMBWI) to extract water bodies in complex environments from Sentinel-2 satellite imagery. Individual tests explore the effectiveness of the SMBWI in eliminating interference of various special interfering cover features. The Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) method and confusion matrix along with the derived accuracy evaluation indicators are used to provide a threshold reference when extracting water bodies and evaluate the accuracy of the water body extraction results, respectively. The SMBWI and eight other commonly used water indices are qualitatively and quantitatively compared through vision and accuracy evaluation indicators, respectively. Here, the SMBWI is proven to be the most effective at suppressing interference of buildings and their shadows, cultivated lands, vegetation, clouds and their shadows, alpine terrain with bare ground and glaciers when extracting water bodies. The overall accuracy in all tests was consistently greater than 96.5%. The SMBWI is proven to have a high ability to identify mixed pixels of water and non-water, with the lowest total error among nine water indices. Most notably, better results are obtained when extracting water bodies under interfering environments of cover features. Therefore, we propose that our novel and robust water index, the SMBWI, is ready to be used for mapping land surface water with high accuracy.

Evaluating the Sustainable Development Science Satellite 1 (SDGSAT-1) Multi-Spectral Data for River Water Mapping: A Comparative Study with Sentinel-2

SDGSAT-1, the first scientific satellite dedicated to advancing the United Nations 2030 Agenda for Sustainable Development, brings renewed vigor and opportunities to water resource monitoring and research. This study evaluates the effectiveness of SDGSAT-1 in extracting water bodies in comparison to Sentinel-2 multi-spectral imager (MSI) data. We applied a confidence thresholding method to delineate river water from land, utilizing the Normalized Differential Water Body Index (NDWI), Normalized Difference Water Index (MNDWI), and Shaded Water Body Index (SWI). It was found that the SWI works best for SDGSAT-1 while the NDWI works best for Sentinel-2. Specifically, the NDWI demonstrates proficiency in delineating a broader spectrum of water bodies and the MNDWI effectively mitigates the impact of shadows, while SDGSAT-1’s SWI extraction of rivers offers high precision, clear outlines, and shadow exclusion. SDGSAT-1’s SWI overall outperforms Sentinel-2’s NDWI in water extraction accuracy (overall accuracy: 90% vs. 91%, Kappa coefficient: 0.771 vs. 0.416, and F1 value: 0.844 vs. 0.651), likely due to its deep blue bands. This study highlights the comprehensive advantages of SDGSAT-1 data in extracting river water bodies, providing a theoretical basis for future research.

Comparative analysis of multispectral data between GF-1 WFV4 and GF-6 WFV sensors

ABSTRACT In recent years, China has launched a number of Gaofen (GF) series earth observation satellites. It is crucial to understand the relationship between the data of the GF series of satellite sensors for the selection of sensor images for scientific research. Taking the same-day transit image pairs of three regions as the study data, this paper compares the consistency of the Top of Atmosphere (TOA) reflectance of GF-1 WFV4 and GF-6 WFV sensors by using the TOA mean comparison method, and discusses the differences in water body and vegetation extraction. The results indicate that the satellite signal intensity in different land cover types and areas differs significantly. In the bare soil-dominated region, GF-1 WFV4 has a larger signal strength than GF-6 WFV, while in the vegetation-dominated region, it turns out to be just the opposite. The difference between the two sensors is mainly related to the difference in the spectral response function and the radiometric resolution of the two satellites. In addition, the determination coefficient (R2) of the corresponding bands of the two sensors is all greater than 0.90, indicating that the two sensors have strong linear correlation and good complementarity.

SwinDefNet: A Novel Surface Water Mapping Model in Mountain and Cloudy Regions Based on Sentinel-2 Imagery

Surface water plays a pivotal role in the context of climate change, human activities, and ecosystems, underscoring the significance of precise monitoring and observation of surface water bodies. However, the intricate and diverse nature of surface water distribution poses substantial challenges to accurate mapping. The extraction of water bodies from medium-resolution satellite remote sensing images using CNN methods is constrained by limitations in receptive fields and inadequate context modeling capabilities, resulting in the loss of boundary details of water bodies and suboptimal fusion of multi-scale features. The existing research on this issue is limited, necessitating the exploration of novel deep-learning network combinations to overcome these challenges. This study introduces a novel deep learning network combination, SwinDefNet, which integrates deformable convolution and Swin Transformer for the first time. By enhancing the effective receptive field and integrating global semantic information, the model can effectively capture the diverse features of water bodies at various scales, thereby enhancing the accuracy and completeness of water extraction. The model was evaluated on Sentinel-2 satellite images, achieving an overall accuracy of 97.89%, an F1 score of 92.33%, and, notably, an accuracy of 98.03% in mountainous regions. These findings highlight the promising potential of this combined approach for precise water extraction tasks.

Identifying Temporal Change in Urban Water Bodies Using OpenStreetMap and Landsat Imagery: A Study of Hangzhou City

As one of the most important ecosystems, the water body is losing water during the rapid development of the city. To understand the impacts on water body change during the rapid urbanization period, this study combines data from the OpenStreetMap platform with Landsat 5/Thematic Mapper images to effectively and accurately identify small urban water bodies. The findings indicate that the trained U-net convolutional neural network (U-Net) water body extraction model and loss function combining Focal Loss and Dice Loss adopted in this study demonstrate high precision in identifying water bodies within the main urban area of Hangzhou, with an accuracy rate of 94.3%. Trends of decrease in water areas with a continuous increase in landscape fragmentation, particularly for the plain river network, were observed from 1985 to 2010, indicating a weaker connection between water bodies resulting from rapid urbanization. Large patches of water bodies, such as natural lakes and big rivers, located at divisions at the edge of the city are susceptible to disappearing during the rapid outward expansion. However, due to the limitations and strict control of development, water bodies, referring to as wetland, slender canals, and plain river networks, in the traditional center division of the city, are preserved well. Combined with the random forest classification method and the U-Net water body extraction model, land use changes from 1985 to 2010 are calculated. Reclamation along the Qiantang River accounts for the largest conversion area between water bodies and cultivated land, constituting more than 90% of the total land use change area, followed by the conversion of water bodies into construction land, particularly in the northeast of Xixi Wetland. Notably, the conversion of various land use types within Xixi Wetland into construction land plays a significant role in the rise of the carbon footprint.

Water body extraction from high spatial resolution remote sensing images based on enhanced U-Net and multi-scale information fusion

Employing deep learning techniques for the semantic segmentation of remote sensing images has emerged as a prevalent approach for acquiring information about water bodies. Yet, current models frequently fall short in accurately extracting water bodies from high-resolution remote sensing images, as these images often present intricate details of terrestrial objects and complex backgrounds. Vegetation, shadows, and other objects close to water boundaries have increased similarity to water bodies. Moreover, water bodies in high-resolution images have different boundary complexities, shapes, and sizes. This situation makes it somewhat challenging to accurately distinguish water bodies in high-resolution images. To overcome these difficulties, this paper presents a novel network model named EU-Net, specifically designed to extract water bodies from high-resolution remote sensing images. The proposed EU-Net model, with U-net as the backbone network, incorporates improved residual connections and attention mechanisms, and designs multi-scale dilated convolution and multi-scale feature fusion modules to enhance water body extraction performance in complex scenarios. Specifically, in the proposed model, improved residual connections are introduced to enable the learning of more complex features; the attention mechanism is employed to improve the model's discriminative ability by focusing on important channels and spatial areas. The implemented multi-scale dilated convolution technique enhances the model's receptive field while maintaining the same number of parameters. The designed multi-scale feature fusion module is capable of processing both small-scale details and large-scale structures in images, while simultaneously modeling the spatial context relationships of features at different scales. Experimental results validate the superior performance of EU-Net in accurately identifying water bodies from high-resolution remote sensing images, outperforming current models in terms of water extraction accuracy.

A dataset of surface water area with 12-day resolution in Lijiang River Basin from 2015 to 2022

Widely distributed in Guangxi, Karst landforms are characterized by heavy rainfall during the flood season, rapid hydrological dynamics, and notable spatiotemporal variations. A water is an important ecological foundation and natural resource, the timely and accurate acquisition of spatiotemporal changes in surface water bodies holds practical significance for the protection and management of water resources, rapid assessment of drought and flood disasters, and the realization of sustainable development across various economic industries. This dataset takes the Lijiang River Basin in Guilin, Guangxi as the research area. Addressing prevalent challenges such as infrequenct surface water monitoring, limited water body extraction methods due to sparse sample information, and low extraction accuracy in complex terrain areas, we combined the data from Sentinel-1 radar and Sentinel-2 in the study. Considering the different imaging principles of radar and optical sensors, we developed a vector classification method based on empirical thresholds for optical and other multi-source remote sensing data. The method can effectively eliminate interference of most ground object shadows, allowing for the construction of dataset of surface water area with 12-day resolution in Lijiang River Basin from 2015 to 2022. The dataset comprises a total of 196 periods of surface water body vector data. According to the results after verifying the spatial distribution and quantitative accuracy of the dataset, the dataset demonstrates its capability to accurately extract inlets and outlets of rivers, lakes and reservoirs, as well as small water bodies in complex terrain areas. The overall accuracy reaches 92.73%,with a Kappa coefficient of 0.85. This dataset can be used to support water resources protection and sustainable management of the ecological environment in the Lijiang River Basin. Additionally, it can provide reliable data for decision-making in areas such as emergency management, disaster prevention, water conservancy development, and economic development.

Extraction of water bodies from high-resolution remote sensing imagery based on a deep semantic segmentation network

The precise delineation of urban aquatic features is of paramount importance in scrutinizing water resources, monitoring floods, and devising water management strategies. Addressing the challenge of indistinct boundaries and the erroneous classification of shadowed regions as water in high-resolution remote sensing imagery, we introduce WaterDeep, which is a novel deep learning framework inspired by the DeepLabV3 + architecture and an innovative fusion mechanism for high- and low-level features. This methodology first creates a comprehensive dataset of high-resolution remote sensing images, then progresses through the Xception baseline network for low-level feature extraction, and harnesses densely connected Atrous Spatial Pyramid Pooling (ASPP) modules to assimilate multi-scale data into sophisticated high-level features. Subsequently, the network decoder amalgamates the elemental and intricate features and applies dual-line interpolation to the amalgamated dataset to extract aqueous formations from the remote images. Experimental evidence substantiates that WaterDeep outperforms its existing deep learning counterparts, achieving a stellar overall accuracy of 99.284%, FWIoU of 95.58%, precision of 97.562%, recall of 95.486%, and F1 score of 96.513%. It also excels in the precise demarcation of edges and the discernment of shadows cast by urban infrastructure. The superior efficacy of the proposed method in differentiating water bodies in complex urban environments has significant practical applications in real-world contexts.