Rui Li, Shunyi Zheng, Chenxi Duan, Ce Zhang
In this paper, a Multi-Scale Fully Convolutional Network (MSFCN) with multi-scale convolutional kernel is proposed to exploit discriminative representations from two-dimensional (2D) satellite images.
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Extracting cultivated land accurately from high-resolution remote images is a basic task for precision agriculture. This report introduces our solution to the iFLYTEK challenge 2021 cultivated land extraction from high-resolution remote sensing image. The challenge requires segmenting cultivated land objects in very high-resolution multispectral remote sensing images. We established a highly effective and efficient pipeline to solve this problem. We first divided the original images into small tiles and separately performed instance segmentation on each tile. We explored several instance segme…
As an important application in remote sensing, landcover classification remains one of the most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly increasing number of Deep Learning (DL) based landcover methods and training strategies are claimed to be the state-of-the-art, the already fragmented technical landscape of landcover mapping methods has been further complicated. Although there exists a plethora of literature review work attempting to guide researchers in making an informed choice of landcover mapping methods, the articles either focus on the review of ap…
Sea-land segmentation is an important process for many key applications in remote sensing. Proper operative sea-land segmentation for remote sensing images remains a challenging issue due to complex and diverse transition between sea and lands. Although several Convolutional Neural Networks (CNNs) have been developed for sea-land segmentation, the performance of these CNNs is far from the expected target. This paper presents a novel deep neural network structure for pixel-wise sea-land segmentation, a Residual Dense U-Net (RDU-Net), in complex and high-density remote sensing images. RDU-Net is…
Land-cover classification has long been a hot and difficult challenge in remote sensing community. With massive High-resolution Remote Sensing (HRS) images available, manually and automatically designed Convolutional Neural Networks (CNNs) have already shown their great latent capacity on HRS land-cover classification in recent years. Especially, the former can achieve better performance while the latter is able to generate lightweight architecture. Unfortunately, they both have shortcomings. On the one hand, because manual CNNs are almost proposed for natural image processing, it becomes very…
Land use and land cover (LULC) classification using remote sensing imagery plays a vital role in many environment modeling and land use inventories. In this study, a hybrid feature optimization algorithm along with a deep learning classifier is proposed to improve the performance of LULC classification, helping to predict wildlife habitat, deteriorating environmental quality, haphazard, etc. LULC classification is assessed using Sat 4, Sat 6 and Eurosat datasets. After the selection of remote sensing images, normalization and histogram equalization methods are used to improve the quality of th…
RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images
本文提出了一种基于残差多头注意力 U-Net 架构的端到端深度学习模型 RMAU-NET，利用遥感图像进行滑坡检测与分割，在多个基准数据集上取得了优异的 F1 和 mIoU 分数，证明了其在实际滑坡观测系统中的应用潜力。
Multi-modality data is becoming readily available in remote sensing (RS) and can provide complementary information about the Earth's surface. Effective fusion of multi-modal information is thus important for various applications in RS, but also very challenging due to large domain differences, noise, and redundancies. There is a lack of effective and scalable fusion techniques for bridging multiple modality encoders and fully exploiting complementary information. To this end, we propose a new multi-modality network (MultiModNet) for land cover mapping of multi-modal remote sensing data based o…
Detecting and masking cloud and cloud shadow from satellite remote sensing images is a pervasive problem in the remote sensing community. Accurate and efficient detection of cloud and cloud shadow is an essential step to harness the value of remotely sensed data for almost all downstream analysis. DeepMask, a new algorithm for cloud and cloud shadow detection in optical satellite remote sensing imagery, is proposed in this study. DeepMask utilizes ResNet, a deep convolutional neural network, for pixel-level cloud mask generation. The algorithm is trained and evaluated on the Landsat 8 Cloud Co…
Landcover classification and change detection using remote sensing and machine learning: a case study of Western Fiji
本研究针对斐济快速城镇化背景，提出结合遥感与机器学习的框架，利用Landsat-8影像、Google Earth Engine及k-means、卷积神经网络等方法，完成楠迪2013-2024年土地覆盖分类与变化检测，为相关建模与监测提供技术支持。
We explore the use of convolutional neural networks for the semantic classification of remote sensing scenes. Two recently proposed architectures, CaffeNet and GoogLeNet, are adopted, with three different learning modalities. Besides conventional training from scratch, we resort to pre-trained networks that are only fine-tuned on the target data, so as to avoid overfitting problems and reduce design time. Experiments on two remote sensing datasets, with markedly different characteristics, testify on the effectiveness and wide applicability of the proposed solution, which guarantees a significa…
DepthSeg: Depth prompting in remote sensing semantic segmentation
针对现有遥感语义分割方法忽略高程差异导致复杂场景分类错误的问题，提出DepthSeg框架，通过深度提示整合深度信息，结合轻量适配器微调与特征耦合，提升了土地覆盖制图精度。
Land-cover classification using remote sensing imagery is an important Earth observation task. Recently, land cover classification has benefited from the development of fully connected neural networks for semantic segmentation. The benchmark datasets available for training deep segmentation models in remote sensing imagery tend to be small, however, often consisting of only a handful of images from a single location with a single scale. This limits the models' ability to generalize to other datasets. Domain adaptation has been proposed to improve the models' generalization but we find these ap…
DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for Urban Climate Resilience
该研究提出面向城市气候韧性的遥感深度学习架构DeepLCZChange，关联机载LiDAR与Landsat 8温度数据，并通过纽约市的概念验证实验验证了城市森林的降温效应。
Remote Sensing and Machine Learning-Based Analysis of Land Use and Vegetation Change in Dhaka District, Bangladesh
本研究结合遥感与机器学习，分析孟加拉国达卡区2019-2024年土地利用与植被变化，发现建成区大幅扩张、植被与水体缩减，随机森林分类精度最高，为城市可持续发展提供数据支撑。
Estimating forest carbon stocks from high-resolution remote sensing imagery by reducing domain shift with style transfer
本研究利用风格迁移减少域偏移，结合Swin Transformer从高分辨率遥感影像估算森林碳储量，以提升监测精度。
Generalized Few-Shot Meets Remote Sensing: Discovering Novel Classes in Land Cover Mapping via Hybrid Semantic Segmentation Framework
针对土地覆盖制图中新类别发现难的问题，提出基于广义少样本分割的SegLand框架，结合多基础学习器与改进POP网络，在OpenEarthMap挑战赛中获第一，能高效更新新类别。
Sustainability of the global environment is dependent on the accurate land cover information over large areas. Even with the increased number of satellite systems and sensors acquiring data with improved spectral, spatial, radiometric and temporal characteristics and the new data distribution policy, most existing land cover datasets were derived from a pixel-based single-date multi-spectral remotely sensed image with low accuracy. To improve the accuracy, the bottleneck is how to develop an accurate and effective image classification technique. By incorporating and utilizing the complete mult…
Deep learning approaches have shown promising results in remote sensing high spatial resolution (HSR) land-cover mapping. However, urban and rural scenes can show completely different geographical landscapes, and the inadequate generalizability of these algorithms hinders city-level or national-level mapping. Most of the existing HSR land-cover datasets mainly promote the research of learning semantic representation, thereby ignoring the model transferability. In this paper, we introduce the Land-cOVEr Domain Adaptive semantic segmentation (LoveDA) dataset to advance semantic and transferable …
A Deep-Learning Framework for Land-Sliding Classification from Remote Sensing Image
本文提出了一种结合数据增强、EfficientNet_Large模型和SVM分类器的深度学习框架，用于从遥感图像中自动检测滑坡，实现了较高的分类性能。
OpenStreetMap (OSM) is a community-based, freely available, editable map service that was created as an alternative to authoritative ones. Given that it is edited mainly by volunteers with different mapping skills, the completeness and quality of its annotations are heterogeneous across different geographical locations. Despite that, OSM has been widely used in several applications in {Geosciences}, Earth Observation and environmental sciences. In this work, we present a review of recent methods based on machine learning to improve and use OSM data. Such methods aim either 1) at improving the …
Integrated Dynamic Phenological Feature for Remote Sensing Image Land Cover Change Detection
本文提出InPhea模型，通过集成物候特征、差分注意力模块和多阶段对比学习，解决遥感影像变化检测中由物候引起的类内伪变化问题，并在多个数据集上取得优于现有模型的效果。
Landslide Detection and Segmentation Using Remote Sensing Images and Deep Neural Network
本文提出基于深度神经网络的滑坡检测与分割系统，通过特征工程、残差卷积、注意力机制、多分辨率集成和组合损失改进U-Net，在Landslide4Sense上F1达84.07，mIoU达76.07。
In present days remote sensing is most used application in many sectors. This remote sensing uses different images like multispectral, hyper spectral or ultra spectral. The remote sensing image classification is one of the significant method to classify image. In this state we classify the maximum likelihood classification with fuzzy logic. In this we experimenting fuzzy logic like spatial, spectral texture methods in that different sub methods to be used for image classification.
In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are available for land-cover mapping. However, due to the complex information brought by the increased spatial resolution and the data disturbances caused by different conditions of image acquisition, it is often difficult to find an efficient method for achieving accurate land-cover classification with high-resolution and heterogeneous remote sensing images. In this paper, we propose a scheme to apply deep model obtained from labeled land-cover dataset to classify unlabeled HRRS images. The main idea is to r…
Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover
本文比较DeeplabV3+与动态加权改进的CPS等监督、半监督LULC分割方法，并结合Cartosat MX大气校正和海得拉巴案例验证其城市变化监测价值。
UniDiff: Parameter-Efficient Adaptation of Diffusion Models for Land Cover Classification with Multi-Modal Remotely Sensed Imagery and Sparse Annotations
UniDiff通过参数高效的方法适应扩散模型，实现多模态遥感数据在稀疏标注下的有效分类与融合。
Satellite remote imaging enables the detailed study of land use patterns on a global scale. We investigate the possibility to improve the information content of traditional land use classification by identifying the nature of industrial sites from medium-resolution remote sensing images. In this work, we focus on classifying different types of power plants from Sentinel-2 imaging data. Using a ResNet-50 deep learning model, we are able to achieve a mean accuracy of 90.0% in distinguishing 10 different power plant types and a background class. Furthermore, we are able to identify the cooling me…
LALE: Lightweight-Transformer Architecture for Land-Cover Estimation
提出LALE架构，通过分辨率分支编码器（轻量级ConvMixer处理高分辨率局部特征，Transformer处理低分辨率全局上下文）和全MLP多尺度解码器，在遥感图像分割中实现高效性能与计算成本的平衡。
Mapping Farmed Landscapes from Remote Sensing
针对农业景观缺乏大规模生态地图的问题，本文提出Farmscapes，利用深度学习分割模型生成覆盖英格兰的高分辨率乡村景观地图，为栖息地恢复和生物多样性监测提供开放工具。
Convolutional neural networks for mineral prospecting through alteration mapping with remote sensing data
本研究利用CNN结合Landsat 8、Landsat 9和ASTER遥感数据，对澳大利亚布罗肯希尔北部蚀变带进行填图，通过地面真值训练和选择性PCA方法，证明CNN在捕捉空间模式上优于传统机器学习模型，并能有效识别细微矿化相关蚀变。
Global-Local Contextual Progressive Expansion Network for Martian Landslide Segmentation in Multimodal Remote Sensing Imagery
针对火星滑坡分割中形态多变与前景稀疏的挑战，本文提出结合上下文渐进层扩展与Transformer推理的U形网络TransCPLES，在多模态数据集MMLSv2上取得最佳性能。
SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection
针对遥感数据获取难、标注贵的问题，提出大规模合成数据集SyntheWorld，含4万幅亚米级图像及八类土地覆盖标注和4万对双时相建筑变化标注，经多基准验证有效，将开源以促进遥感研究。
Maps are used to describe far-off places . It is an aid for navigation and military strategies. Mapping of the lands are important and the mapping work is based on (i). Natural resource management & development (ii). Information technology ,(iii). Environmental development ,(iv). Facility management and (v). e-governance. The Landuse / Landcover system espoused by almost all Organisations and scientists, engineers and remote sensing community who are involved in mapping of earth surface features, is a system which is derived from the united States Geological Survey (USGS) LULC classification s…
Remote sensing techniques are widely used for land cover classification and urban analysis. The availability of high resolution remote sensing imagery limits the level of classification accuracy attainable from pixel-based approach. In this paper object-based classification scheme based on a hierarchical support vector machine is introduced. By combining spatial and spectral information, the amount of overlap between classes can be decreased; thereby yielding higher classification accuracy and more accurate land cover maps. We have adopted certain automatic approaches based on the advanced tec…
An Iterative Classification and Semantic Segmentation Network for Old Landslide Detection Using High-Resolution Remote Sensing Images
针对古滑坡形态难辨与小样本难题，提出ICSSN，通过共享特征提取器、OCL、SOCL及迭代训练融合语义特征，显著提升检测分类与分割性能。
In this study, a novel machine learning algorithm is presented for disaggregation of satellite soil moisture (SM) based on self-regularized regressive models (SRRM) using high-resolution correlated information from auxiliary sources. It includes regularized clustering that assigns soft memberships to each pixel at fine-scale followed by a kernel regression that computes the value of the desired variable at all pixels. Coarse-scale remotely sensed SM were disaggregated from 10km to 1km using land cover, precipitation, land surface temperature, leaf area index, and in-situ observations of SM. Th…
Land cover classification is a multi-class segmentation task to classify each pixel into a certain natural or man-made category of the earth surface, such as water, soil, natural vegetation, crops, and human infrastructure. Limited by hardware computational resources and memory capacity, most existing studies preprocessed original remote sensing images by down sampling or cropping them into small patches less than 512*512 pixels before sending them to a deep neural network. However, down sampling images incurs spatial detail loss, renders small segments hard to discriminate, and reverses the s…
Long-Range Correlation Supervision for Land-Cover Classification from Remote Sensing Images
针对遥感图像语义分割中长距离依赖缺乏显式监督的问题，提出监督式长距离相关性网络SLCNet，利用类别一致性信息进行监督，并结合自适应感受野模块与混合损失函数，在三个数据集上实现最先进的土地覆盖分类性能。
With the advancement of remote-sensed imaging large volumes of very high resolution land cover images can now be obtained. Automation of object recognition in these 2D images, however, is still a key issue. High intra-class variance and low inter-class variance in Very High Resolution (VHR) images hamper the accuracy of prediction in object recognition tasks. Most successful techniques in various computer vision tasks recently are based on deep supervised learning. In this work, a deep Convolutional Neural Network (CNN) based on symmetric encoder-decoder architecture with skip connections is e…
SPEX: A Vision-Language Model for Land Cover Extraction on Spectral Remote Sensing Images
SPEX是一种专为光谱遥感图像土地覆盖提取设计的多模态视觉-语言模型，通过多尺度特征聚合和多光谱视觉预训练等技术，实现了精确的像素级解释和可解释的预测生成。
As remote sensing (RS) data obtained from different sensors become available largely and openly, multimodal data processing and analysis techniques have been garnering increasing interest in the RS and geoscience community. However, due to the gap between different modalities in terms of imaging sensors, resolutions, and contents, embedding their complementary information into a consistent, compact, accurate, and discriminative representation, to a great extent, remains challenging. To this end, we propose a shared and specific feature learning (S2FL) model. S2FL is capable of decomposing mult…
Many significant applications need land cover information of remote sensing images that are acquired from different areas and times, such as change detection and disaster monitoring. However, it is difficult to find a generic land cover classification scheme for different remote sensing images due to the spectral shift caused by diverse acquisition condition. In this paper, we develop a novel land cover classification method that can deal with large-scale data captured from widely distributed areas and different times. Additionally, we establish a large-scale land cover classification dataset …
Modeling forests using historical data allows for more accurately evolution analysis, thus providing an important basis for other studies. As a recognized and effective tool, remote sensing plays an important role in forestry analysis. We can use it to derive information about the forest, including tree type, coverage and canopy density. There are many forest time series modeling studies using statistic values, but few using remote sensing images. Image prediction digital twin is an implementation of digital twin, which aims to predict future images bases on historical data. In this paper, we …
Efficiently implementing remote sensing image classification with high spatial resolution imagery can provide a significant value in Land Use and Land Cover (LULC) classification. The new advances in remote sensing and deep learning technologies have facilitated the extraction of spatiotemporal information for LULC classification. Moreover, the diverse disciplines of science, including remote sensing, have utilised tremendous improvements in image classification by Convolutional Neural Networks (CNNs) with transfer learning. In this study, instead of training CNNs from scratch, the transfer le…
Robust semantic segmentation of VHR remote sensing images from UAV sensors is critical for earth observation, land use, land cover or mapping applications. Several factors such as shadows, weather disruption and camera shakes making this problem highly challenging, especially only using RGB images. In this paper, we propose the use of multi-modality data including NIR, RGB and DSM to increase robustness of segmentation in blurred or partially damaged VHR remote sensing images. By proposing a cascaded dense encoder-decoder network and the SELayer based fusion and assembling techniques, the prop…
We propose a novel convolutional neural network architecture for estimating geospatial functions such as population density, land cover, or land use. In our approach, we combine overhead and ground-level images in an end-to-end trainable neural network, which uses kernel regression and density estimation to convert features extracted from the ground-level images into a dense feature map. The output of this network is a dense estimate of the geospatial function in the form of a pixel-level labeling of the overhead image. To evaluate our approach, we created a large dataset of overhead and groun…
MMLSv2: A Multimodal Dataset for Martian Landslide Detection in Remote Sensing Imagery
MMLSv2是一个用于火星滑坡检测的多模态数据集，包含七种波段的图像，用于评估模型在不同地理区域的泛化能力和鲁棒性。
Building coverage statistics provide crucial insights into the urbanization, infrastructure, and poverty level of a region, facilitating efforts towards alleviating poverty, building sustainable cities, and allocating infrastructure investments and public service provision. Global mapping of buildings has been made more efficient with the incorporation of deep learning models into the pipeline. However, these models typically rely on high-resolution satellite imagery which are expensive to collect and infrequently updated. As a result, building coverage data are not updated timely especially i…
Change Detection Between Optical Remote Sensing Imagery and Map Data via Segment Anything Model (SAM)
本文利用SAM的零样本分割能力，在分割域中直接比较光学遥感影像与OSM数据，并通过无提示和框/掩码提示两种策略实现无监督多模态变化检测，在三个数据集上取得优于代表性方法的结果。
Vision Transformers and Convolutional Neural Networks for Land Use Scene Classification
本文比较了视觉Transformer和CNN在遥感土地利用场景分类中的性能，发现CNN在有限训练样本和局部纹理特征强的场景中表现稳健，而ViT在数据充足时能更好地捕捉全局空间关系，但计算成本更高。
AgriPotential: A Novel Multi-Spectral and Multi-Temporal Remote Sensing Dataset for Agricultural Potentials
AgriPotential是一种用于农业潜力预测的新型多光谱和多时相遥感数据集，支持多种机器学习任务，旨在提升可持续土地利用规划的效率。
Changes to Captions: An Attentive Network for Remote Sensing Change Captioning
本文提出Chg2Cap注意力网络，结合Siamese CNN、分层自注意力和Transformer，用于双时相遥感图像变化描述，并在两个数据集上验证其有效性。
Seeking Consensus: Geometric-Semantic On-the-Fly Recalibration for Open-Vocabulary Remote Sensing Semantic Segmentation
本文提出SeeCo框架，通过几何和语义双共识学习提升遥感开放词汇语义分割性能，无需训练即可实现场景特异性重校准。
The production of thematic maps depicting land cover is one of the most common applications of remote sensing. To this end, several semantic segmentation approaches, based on deep learning, have been proposed in the literature, but land cover segmentation is still considered an open problem due to some specific problems related to remote sensing imaging. In this paper we propose a novel approach to deal with the problem of modelling multiscale contexts surrounding pixels of different land cover categories. The approach leverages the computation of a heteroscedastic measure of uncertainty when …
With the rapid progress of China's urbanization, research on the automatic detection of land-use patterns in Chinese cities is of substantial importance. Deep learning is an effective method to extract image features. To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features. Using the Google Tensorflow framework, a powerful convolution neural network (CNN) library was created. First, the transferred model was previously trained on ImageNet, one of the largest object-im…
In this paper, we propose an unsupervised method for hyperspectral remote sensing image segmentation. The method exploits the mean-shift clustering algorithm that takes as input a preliminary hyperspectral superpixels segmentation together with the spectral pixel information. The proposed method does not require the number of segmentation classes as input parameter, and it does not exploit any a-priori knowledge about the type of land-cover or land-use to be segmented (e.g. water, vegetation, building etc.). Experiments on Salinas, SalinasA, Pavia Center and Pavia University datasets are carri…
Multispectral Remote Sensing for Weed Detection in West Australian Agricultural Lands
针对西澳大利亚孔迪宁地区杂草滋生的农业挑战，本研究构建定制多光谱遥感数据集，提出端到端杂草检测框架，通过无人机采集数据并结合植被指数与深度学习模型，ResNet模型实现高精度检测，推进精准农业实践。
Moonstone: A Multimodal Foundation Model and Benchmark for Lunar Remote Sensing
针对月球多模态遥感数据分散且无评估机器学习基准的问题，引入月光石基准。贡献包括构建全球月球预训练数据集，提出MG-MAE模型，创建涵盖多任务的基准，其预训练特征在各任务中表现出色。
Adaptive Frequency Enhancement Network for Remote Sensing Image Semantic Segmentation
针对遥感图像语义分割中网络参数难以适应不同地物分布及空间与频域特征交互不足的问题，提出自适应频率增强网络AFENet，通过AFSIM和SFM模块动态调制频率特征并融合全局与局部信息，在三个数据集上取得优于现有方法的性能。
Spatio-Temporal driven Attention Graph Neural Network with Block Adjacency matrix (STAG-NN-BA) for Remote Land-use Change Detection
本文提出STAG-NN-BA模型，利用超像素和块邻接矩阵实现遥感土地利用变化检测，优于传统图和非图基线。
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