The cotton boll weevil, Anthonomus grandis Boheman is a serious pest to the U.S. cotton industry that has cost more than 16 billion USD in damages since it entered the United States from Mexico in the late 1800s. This pest has been nearly eradicated; however, southern part of Texas still faces this issue and is always prone to the pest reinfestation each year due to its sub-tropical climate where cotton plants can grow year-round. Volunteer cotton (VC) plants growing in the fields of inter-seasonal crops, like corn, can serve as hosts to these pests once they reach pin-head square stage (5-6 leaf stage) and therefore need to be detected, located, and destroyed or sprayed . In this paper, we present a study to detect VC plants in a corn field using YOLOv3 on three band aerial images collected by unmanned aircraft system (UAS). The two-fold objectives of this paper were : (i) to determine whether YOLOv3 can be used for VC detection in a corn field using RGB (red, green, and blue) aerial images collected by UAS and (ii) to investigate the behavior of YOLOv3 on images at three different scales (320 x 320, S1; 416 x 416, S2; and 512 x 512, S3 pixels) based on average precision (AP), mean average precision (mAP) and F1-score at 95% confidence level. No significant differences existed for mAP among the three scales, while a significant difference was found for AP between S1 and S3 (p = 0.04) and S2 and S3 (p = 0.02). A significant difference was also found for F1-score between S2 and S3 (p = 0.02). The lack of significant differences of mAP at all the three scales indicated that the trained YOLOv3 model can be used on a computer vision-based remotely piloted aerial application system (RPAAS) for VC detection and spray application in near real-time.

Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance. It is being broadly used to monitor ecosystems, mainly for their preservation. Ever-growing reports of invasive species have affected the natural balance of ecosystems. Exotic invasive species have a critical impact when introduced into new ecosystems and may lead to the extinction of native species. In this study, we focus on Ludwigia peploides, considered by the European Union as an aquatic invasive species. Its presence can negatively impact the surrounding ecosystem and human activities such as agriculture, fishing, and navigation. Our goal was to develop a method to identify the presence of the species. We used images collected by a drone-mounted multispectral sensor to achieve this, creating our LudVision data set. To identify the targeted species on the collected images, we propose a new method for detecting Ludwigia p. in multispectral images. The method is based on existing state-of-the-art semantic segmentation methods modified to handle multispectral data. The proposed method achieved a producer's accuracy of 79.9% and a user's accuracy of 95.5%.

Analyzing the planet at scale with satellite imagery and machine learning is a dream that has been constantly hindered by the cost of difficult-to-access highly-representative high-resolution imagery. To remediate this, we introduce here the WorldStrat dataset. The largest and most varied such publicly available dataset, at Airbus SPOT 6/7 satellites' high resolution of up to 1.5 m/pixel, empowered by European Space Agency's Phi-Lab as part of the ESA-funded QueryPlanet project, we curate nearly 10,000 sqkm of unique locations to ensure stratified representation of all types of land-use across the world: from agriculture to ice caps, from forests to multiple urbanization densities. We also enrich those with locations typically under-represented in ML datasets: sites of humanitarian interest, illegal mining sites, and settlements of persons at risk. We temporally-match each high-resolution image with multiple low-resolution images from the freely accessible lower-resolution Sentinel-2 satellites at 10 m/pixel. We accompany this dataset with an open-source Python package to: rebuild or extend the WorldStrat dataset, train and infer baseline algorithms, and learn with abundant tutorials, all compatible with the popular EO-learn toolbox. We hereby hope to foster broad-spectrum applications of ML to satellite imagery, and possibly develop from free public low-resolution Sentinel2 imagery the same power of analysis allowed by costly private high-resolution imagery. We illustrate this specific point by training and releasing several highly compute-efficient baselines on the task of Multi-Frame Super-Resolution. High-resolution Airbus imagery is CC BY-NC, while the labels and Sentinel2 imagery are CC BY, and the source code and pre-trained models under BSD. The dataset is available at https://zenodo.org/record/6810792 and the software package at https://github.com/worldstrat/worldstrat .

New Delhi: Centre of Excellence on Satellite & Unmanned Remote Vehicle Initiative (CoE-SURVEI) has developed an Artificial Intelligence-based software which can automatically detect change on ground, including unauthorised constructions and encroachments in a time series using Satellite Imagery. The CoE-SURVEI, established by Directorate General Defence Estates (DGDE) at National Institute of Defence Estates Management, leverages latest technologies in survey viz. The CoE was inaugurated by Raksha Mantri Rajnath Singh on December 16, 2021. This Change Detection Software has been developed by CoE-SURVEI in collaboration with knowledge partner Bhabha Atomic Research Centre (BARC), Visakhapatnam. Presently, the tool uses National Remote Sensing Centre (NRSC) Cartosat-3 imagery with trained software.

This paper presents MultiSenGE that is a new large scale multimodal and multitemporal benchmark dataset covering one of the biggest administrative region located in the Eastern part of France. MultiSenGE contains 8,157 patches of 256 256 pixels for the Sentinel-2 L2A, Sentinel-1 GRD images in VV-VH polarization and a Regional large scale Land Use/Land Cover (LULC) topographic reference database. With MultiSenGE, we contribute to the recents developments towards shared data use and machine learning methods in the field of environmental science. The purpose of this dataset is to propose relevant and easy-access dataset to explore deep learning methods. We use MultiSenGE to evaluate the performance for urban areas using well-known deep learning techniques. These results serve as a baseline for future research on remote sensing applications using the multi-temporal and multimodal aspects of MultiSenGE. With all patches georeferenced at a 10 meters spatial resolution covering the whole Grand-Est Region, MultiSenGE provides an opportunity for environmental benchmark dataset will help to advance data-driven techniques for land use/land cover remote sensing applications.

Increasingly frequent wildfires significantly affect solar energy production as the atmospheric aerosols generated by wildfires diminish the incoming solar radiation to the earth. Atmospheric aerosols are measured by Aerosol Optical Depth (AOD), and AOD data streams can be retrieved and monitored by geostationary satellites. However, multi-source remote-sensing data streams often present heterogeneous characteristics, including different data missing rates, measurement errors, systematic biases, and so on. To accurately estimate and predict the underlying AOD propagation process, there exist practical needs and theoretical interests to propose a physics-informed statistical approach for modeling wildfire AOD propagation by simultaneously utilizing, or fusing, multi-source heterogeneous satellite remote-sensing data streams. Leveraging a spectral approach, the proposed approach integrates multi-source satellite data streams with a fundamental advection-diffusion equation that governs the AOD propagation process. A bias correction process is included in the statistical model to account for the bias of the physics model and the truncation error of the Fourier series. The proposed approach is applied to California wildfires AOD data streams obtained from the National Oceanic and Atmospheric Administration. Comprehensive numerical examples are provided to demonstrate the predictive capabilities and model interpretability of the proposed approach. Computer code has been made available on GitHub.

Spectral Indices (SIs) are mathematical equations applied to each pixel image to highlight a specific phenomenon on the ground. Most SIs are computed from the reflectance data produced after some pre-processing stages of multispectral remote sensing images. In which bx and by are the reflectance values of a pixel in bands x and y. If we calculate the value of a SI for each pixel, we can generate an image from SI. In this post, I want to talk about the two most important spectral indices and how to calculate them for a case study in the center of Rome, Italy, using the Sentinel-hub cloud platform.

Huanfeng Shen, Wuhan University ([email protected]), Jane Liu, University of Toronto ([email protected]), Wenping Yuan, Sun Yat-Sen University ([email protected]), Yongguang Zhang, Nanjing University ([email protected]), Holly Croft, University of Sheffield ([email protected]), Xiaobin Guan, Wuhan University ([email protected]). The dramatic increase in anthropogenic carbon emissions over the last five decades has already led to substantial damage to our environment, including increases in extreme weatherevents, loss of biodiversity, and a rise in sea level. Carbon neutrality, i.e., net-zero anthropogenic carbon emissions, is necessary to ensure the sustainable future of human beings, and hundreds of countries have pledged to achieve this goal by mid-century. Remote sensing techniques can acquire frequent observations of the Earth with various temporal and spatial resolutions, and provide substantial information for carbon emission monitoring and carbon cycle modeling. Remote sensing observations not only can be directly applied to retrieve the atmospheric concentrations of greenhouse gases (e.g., CO2, CO, CH4, CFCs, O3, et al.), but also can be employed to investigate the carbon budget of natural ecosystems.

Developed by Minderoo Foundation, the'Global Plastic Watch' tool uses advanced satellite data technology and machine learning to create a near-real-time, high resolution map of plastic pollution. The tool aims to help authorities better manage plastic leakage into the marine environment, and is said to provide the largest ever open source dataset of plastic waste across dozens of countries. Global Plastic Watch uses remote sensing satellite imagery from the European Space Agency and a novel machine learning model created in collaboration with digital product agency Earthrise Media. The tool can determine the size and scale of land-based plastic waste sites, which fuel the growing issue of plastic pollution in the world's rivers and oceans. By using the data, governments, industry and communities can evaluate and monitor the risk of land-based plastic waste sites as well as prioritise investment in solutions, Minderoo Foundation said.

This blog is accompanied by a Colab notebook which provides an in-depth look at how Raster Vision works and allows you to run each experiment discussed in this post yourself. Change detection is the computer-vision equivalent of the spot-the-difference game. Given two images, the model must detect all the points at which they differ. In the context of remote sensing, these images are usually satellite or aerial images of the same geographical location at two different points in time. Change detection has been an active research area for a long time and the literature is rich with algorithms that perform the task automatically, ranging from basic image processing techniques to present-day deep neural networks.