scholarly journals Toward Large-Scale Mapping of Tree Crops with High-Resolution Satellite Imagery and Deep Learning Algorithms: A Case Study of Olive Orchards in Morocco

2021 ◽  
Vol 13 (9) ◽  
pp. 1740
Author(s):  
Chenxi Lin ◽  
Zhenong Jin ◽  
David Mulla ◽  
Rahul Ghosh ◽  
Kaiyu Guan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Satellite Imagery ◽  
Large Scale ◽  
Feature Space ◽  
Temporal Information ◽  
Tree Crops ◽  
Climatic Regions ◽  
Multi Temporal ◽  
Olive Trees ◽  
High Resolution Satellite Imagery

Timely and accurate monitoring of tree crop extent and productivities are necessary for informing policy-making and investments. However, except for a very few tree species (e.g., oil palms) with obvious canopy and extensive planting, most small-crown tree crops are understudied in the remote sensing domain. To conduct large-scale small-crown tree mapping, several key questions remain to be answered, such as the choice of satellite imagery with different spatial and temporal resolution and model generalizability. In this study, we use olive trees in Morocco as an example to explore the two abovementioned questions in mapping small-crown orchard trees using 0.5 m DigitalGlobe (DG) and 3 m Planet imagery and deep learning (DL) techniques. Results show that compared to DG imagery whose mean overall accuracy (OA) can reach 0.94 and 0.92 in two climatic regions, Planet imagery has limited capacity to detect olive orchards even with multi-temporal information. The temporal information of Planet only helps when enough spatial features can be captured, e.g., when olives are with large crown sizes (e.g., >3 m) and small tree spacings (e.g., <3 m). Regarding model generalizability, experiments with DG imagery show a decrease in F1 score up to 5% and OA to 4% when transferring models to new regions with distribution shift in the feature space. Findings from this study can serve as a practical reference for many other similar mapping tasks (e.g., nuts and citrus) around the world.

scholarly journals A Large-Scale Dataset and Deep Learning Model for Detecting and Counting Olive Trees in Satellite Imagery

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Amr Abozeid ◽  
Rayan Alanazi ◽  
Ahmed Elhadad ◽  
Ahmed I. Taloba ◽  
Rasha M. Abd El-Aziz
Keyword(s):  
Deep Learning ◽  
Satellite Imagery ◽  
Large Scale ◽  
Estimation Error ◽  
Satellite Image ◽  
Olive Tree ◽  
Learning Model ◽  
Large Area ◽  
Olive Trees ◽  
Deep Learning Model

Since the Pre-Roman era, olive trees have a significant economic and cultural value. In 2019, the Al-Jouf region, in the north of the Kingdom of Saudi Arabia, gained a global presence by entering the Guinness World Records, with the largest number of olive trees in the world. Olive tree detecting and counting from a given satellite image are a significant and difficult computer vision problem. Because olive farms are spread out over a large area, manually counting the trees is impossible. Moreover, accurate automatic detection and counting of olive trees in satellite images have many challenges such as scale variations, weather changes, perspective distortions, and orientation changes. Another problem is the lack of a standard database of olive trees available for deep learning applications. To address these problems, we first build a large-scale olive dataset dedicated to deep learning research and applications. The dataset consists of 230 RGB images collected over the territory of Al-Jouf, KSA. We then propose an efficient deep learning model (SwinTUnet) for detecting and counting olive trees from satellite imagery. The proposed SwinTUnet is a Unet-like network which consists of an encoder, a decoder, and skip connections. Swin Transformer block is the fundamental unit of SwinTUnet to learn local and global semantic information. The results of an experimental study on the proposed dataset show that the SwinTUnet model outperforms the related studies in terms of overall detection with a 0.94% estimation error.

scholarly journals Detection of Tailings Dams Using High-Resolution Satellite Imagery and a Single Shot Multibox Detector in the Jing–Jin–Ji Region, China

2020 ◽  
Vol 12 (16) ◽  
pp. 2626 ◽  
Author(s):  
Qingting Li ◽  
Zhengchao Chen ◽  
Bing Zhang ◽  
Baipeng Li ◽  
Kaixuan Lu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Satellite Imagery ◽  
Spatial Scales ◽  
Detection Accuracy ◽  
Single Shot ◽  
Tailings Dams ◽  
High Resolution Satellite Imagery ◽  
Tailings Ponds

The timely and accurate mapping and monitoring of mine tailings dams is crucial to the improvement of management practices by decision makers and to the prevention of disasters caused by failures of these dams. Due to the complex topography, varying geomorphological characteristics, and the diversity of ore types and mining activities, as well as the range of scales and production processes involved, as they appear in remote sensing imagery, tailings dams vary in terms of their scale, color, shape, and surrounding background. The application of high-resolution satellite imagery for automatic detection of tailings dams at large spatial scales has been barely reported. In this study, a target detection method based on deep learning was developed for identifying the locations of tailings ponds and obtaining their geographical distribution from high-resolution satellite imagery automatically. Training samples were produced based on the characteristics of tailings ponds in satellite images. According to the sample characteristics, the Single Shot Multibox Detector (SSD) model was fine-tuned during model training. The results showed that a detection accuracy of 90.2% and a recall rate of 88.7% could be obtained. Based on the optimized SSD model, 2221 tailing ponds were extracted from Gaofen-1 high resolution imagery in the Jing–Jin–Ji region in northern China. In this region, the majority of tailings ponds are located at high altitudes in remote mountainous areas. At the city level, the tailings ponds were found to be located mainly in Chengde, Tangshan, and Zhangjiakou. The results prove that the deep learning method is very effective at detecting complex land-cover features from remote sensing images.

scholarly journals Penataan Batas Wilayah Administrasi Desa, Hambatan dan Alternatif Solusi dengan Pendekatan Geospasial

1969 ◽  
Vol 12 (2) ◽  
pp. 131-147
Author(s):  
Asadi Asadi
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
High Resolution Satellite Imagery ◽  
Main Instrument ◽  
Topographical Maps ◽  
Imagery Data

Law No. 6 of 2014 concerning Villages provides additional evidence that Indonesia has paid more attention and respect to the existence of villages. The significant amount of village expansion lately is not matched with the clarity of village boundaries that may rise in to potential conflicts. Ideally, the entire instruments to structure village boundaries must first be prepared. One of the instruments needed is the availability of large scale of basic maps (topographical maps) as the main instrument of making a village map. Unfortunately, the large-scale topographical maps are not available yet. This paper provides an alternative acceleration of village boundaries arrangement using High Resolution Satellite Imagery Data that has passed orthorectified process. By involving the community and village leaders in the process of structuring boundaries, and supported by the spirit of fraternity, all problems occured during the activity of village boundaries can be solved with the very best solution.Keywords: village boundary, High Resolution Satellite Imagery Data, spirit of fraternityUndang-Undang Nomor 6 Tahun 2014 tentang Desa memberikan tambahan bukti bahwa negara semakin memperhatikan dan menghormati keberadaan desa. Adanya pemekaran wilayah desa yang signifikan akhir-akhir ini, tidak diimbangi dengan kejelasan batas wilayah desa,berpotensi menimbulkan konflik. Idealnya, seluruh instrumen untuk melakukan penataan batas wilayah desa harus terlebih dahulu disiapkan. Salah satu instrumen tersebut adalah tersedianya peta dasar (peta rupabumi) skala besar sebagai bahan utama pembuatan peta desa. Sayangnya ketersediaan peta rupabumi skala besar belum tersedia. Tulisan ini memberikan alternatif percepatan penataan batas wilayah desa yang dapat menggunakan Citra Satelit Resolusi Tinggi (CSRT) yang sudah melalui proses ortorektifikasi. Dengan melibatkan masyarakat dan tokoh masyarakat desa dalam melakukan proses penataan batas wilayah, dan dengan didukung semangat persaudaraan, diharapkan permasalahan batas wilayah desa dapat diselesaikan dengan sebaik-baiknya.Kata kunci: batas desa, metode kartometrik, CSRT, semangat persaudaraan

scholarly journals Exploring the Potential of High-Resolution Satellite Imagery for the Detection of Soybean Sudden Death Syndrome

2020 ◽  
Vol 12 (7) ◽  
pp. 1213 ◽  
Author(s):  
Muhammad M. Raza ◽  
Chris Harding ◽  
Matt Liebman ◽  
Leonor F. Leandro
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Sudden Death ◽  
Satellite Imagery ◽  
Large Scale ◽  
Vegetation Index ◽  
Sudden Death Syndrome ◽  
Alternative Methods ◽  
High Resolution Satellite Imagery ◽  
Soybean Diseases

Sudden death syndrome (SDS) is one of the major yield-limiting soybean diseases in the Midwestern United States. Effective management for SDS requires accurate detection in soybean fields. Since traditional scouting methods are time-consuming, labor-intensive, and often destructive, alternative methods to monitor SDS in large soybean fields are needed. This study explores the potential of using high-resolution (3 m) PlanetScope satellite imagery for detection of SDS using the random forest classification algorithm. Image data from blue, green, red, and near-infrared (NIR) spectral bands, the calculated normalized difference vegetation index (NDVI), and crop rotation information were used to detect healthy and SDS-infected quadrats in a soybean field experiment with different rotation treatments, located in Boone County, Iowa. Datasets collected during the 2016, 2017, and 2018 soybean growing seasons were analyzed. The results indicate that spectral features, when combined with ground-based information, can detect areas in soybean plots that are at risk for disease, even before foliar symptoms develop. The classification of healthy and diseased soybean quadrats was >75% accurate and the area under the receiver operating characteristic curve (AUROC) was >70%. Our results indicate that high-resolution satellite imagery and random forest analyses have the potential to detect SDS in soybean fields, and that this approach may facilitate large-scale monitoring of SDS (and possibly other economically important soybean diseases). It may also be useful for guiding recommendations for site-specific management in current and future seasons.

Photogrammetric processing and application of multi-temporal satellite imagery stereopairs

2021 ◽  
Vol 974 (8) ◽  
pp. 36-44
Author(s):  
R.V. Permyakov
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Weather Conditions ◽  
3D Models ◽  
High Resolution Imagery ◽  
Multi Temporal ◽  
Unfavourable Weather ◽  
High Resolution Satellite Imagery ◽  
Very High Resolution Imagery ◽  
Very High

Stereopairs of very-high resolution satellite imagery constitute one of the key high-accurate data sources on heights. A stereophotogrammetric technique is a key method of processing these data. Despite that a number of spacecrafts gathering very-high-resolution imagery in a stereo mode constantly increases, the area of the Earth regularly covered by such data and stored in the archives of RSD operators remains relatively small and, as a rule, is limited only to large urban agglomerations. The new collection may not suit the customer for several reasons. Firstly, the materials of the new stereo collection are more expensive than those of the archived one. Secondly, due to unfavourable weather conditions and a busy schedule of satellites, the completion of the new collection may go beyond the deadline specified by the customer. Well known and brand-new criteria to form multi-temporal, stereopairs are analyzed. The specific of photogrammetric processing multi-temporal stereopairs is demonstrated. Application of multi-temporal stereopairs is described. In conclusion it is confirmed that 3D-models and high accurate DTMs can be generated basing on stereo models from multi-temporal satellite imagery in the absence of the following data

scholarly journals Multi-Temporal Sentinel-1 and -2 Data Fusion for Optical Image Simulation

2018 ◽  
Vol 7 (10) ◽  
pp. 389 ◽  
Author(s):  
Wei He ◽  
Naoto Yokoya
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Super Resolution ◽  
Optical Image ◽  
Optical Data ◽  
Image Simulation ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Multi Temporal ◽  
Sar Data

In this paper, we present the optical image simulation from synthetic aperture radar (SAR) data using deep learning based methods. Two models, i.e., optical image simulation directly from the SAR data and from multi-temporal SAR-optical data, are proposed to testify the possibilities. The deep learning based methods that we chose to achieve the models are a convolutional neural network (CNN) with a residual architecture and a conditional generative adversarial network (cGAN). We validate our models using the Sentinel-1 and -2 datasets. The experiments demonstrate that the model with multi-temporal SAR-optical data can successfully simulate the optical image; meanwhile, the state-of-the-art model with simple SAR data as input failed. The optical image simulation results indicate the possibility of SAR-optical information blending for the subsequent applications such as large-scale cloud removal, and optical data temporal super-resolution. We also investigate the sensitivity of the proposed models against the training samples, and reveal possible future directions.

Sign in / Sign up

Export Citation Format

Share Document