With FarmVibes.AI, you can develop rich geospatial insights for agriculture and sustainability. Build models that fuse multiple geospatial and spatiotemporal datasets to obtain insights (e.g. Fusing datasets this way helps generate more robust insights and unlocks new insights that are otherwise not possible without fusion. This repo contains several fusion workflows (published and shown to be key for agriculture related problems) that help you build robust remote sensing, earth observation, and geospatial models with focus on agriculture/farming with ease. Our main focus right now is agriculture and sustainability, which the models are optimized for.

With the new generation of satellite technologies, the archives of remote sensing (RS) images are growing very fast. To make the intrinsic information of each RS image easily accessible, visual question answering (VQA) has been introduced in RS. VQA allows a user to formulate a free-form question concerning the content of RS images to extract generic information. It has been shown that the fusion of the input modalities (i.e., image and text) is crucial for the performance of VQA systems. Most of the current fusion approaches use modality-specific representations in their fusion modules instead of joint representation learning. However, to discover the underlying relation between both the image and question modality, the model is required to learn the joint representation instead of simply combining (e.g., concatenating, adding, or multiplying) the modality-specific representations. We propose a multi-modal transformer-based architecture to overcome this issue. Our proposed architecture consists of three main modules: i) the feature extraction module for extracting the modality-specific features; ii) the fusion module, which leverages a user-defined number of multi-modal transformer layers of the VisualBERT model (VB); and iii) the classification module to obtain the answer. Experimental results obtained on the RSVQAxBEN and RSVQA-LR datasets (which are made up of RGB bands of Sentinel-2 images) demonstrate the effectiveness of VBFusion for VQA tasks in RS. To analyze the importance of using other spectral bands for the description of the complex content of RS images in the framework of VQA, we extend the RSVQAxBEN dataset to include all the spectral bands of Sentinel-2 images with 10m and 20m spatial resolution.

Governments and international organizations the world over are investing towards the goal of achieving universal energy access for improving socio-economic development. However, in developing settings, monitoring electrification efforts is typically inaccurate, infrequent, and expensive. In this work, we develop and present techniques for high-resolution monitoring of electrification progress at scale. Specifically, our 3 unique contributions are: (i) identifying areas with(out) electricity access, (ii) quantifying the extent of electrification in electrified areas (percentage/number of electrified structures), and (iii) differentiating between customer types in electrified regions (estimating the percentage/number of residential/non-residential electrified structures). We combine high-resolution 50 cm daytime satellite images with Convolutional Neural Networks (CNNs) to train a series of classification and regression models. We evaluate our models using unique ground truth datasets on building locations, building types (residential/non-residential), and building electrification status. Our classification models show a 92% accuracy in identifying electrified regions, 85% accuracy in estimating percent of (low/high) electrified buildings within the region, and 69% accuracy in differentiating between (low/high) percentage of electrified residential buildings. Our regressions show $R^2$ scores of 78% and 80% in estimating the number of electrified buildings and number of residential electrified building in images respectively. We also demonstrate the generalizability of our models in never-before-seen regions to assess their potential for consistent and high-resolution measurements of electrification in emerging economies, and conclude by highlighting opportunities for improvement.

Cloud removal is an essential task in remote sensing data analysis. As the image sensors are distant from the earth ground, it is likely that part of the area of interests is covered by cloud. Moreover, the atmosphere in between creates a constant haze layer upon the acquired images. To recover the ground image, we propose to use scattering model for temporal sequence of images of any scene in the framework of low rank and sparse models. We further develop its variant, which is much faster and yet more accurate. To measure the performance of different methods {\em objectively}, we develop a semi-realistic simulation method to produce cloud cover so that various methods can be quantitatively analysed, which enables detailed study of many aspects of cloud removal algorithms, including verifying the effectiveness of proposed models in comparison with the state-of-the-arts, including deep learning models, and addressing the long standing problem of the determination of regularisation parameters. The latter is companioned with theoretic analysis on the range of the sparsity regularisation parameter and verified numerically.

Every day, observation satellites capture terabytes of imagery of the Earth's surface that feed into a wide variety of civil and military applications. This stream of data has grown so large that only automated methods can feasibly analyze it. One critical component of remote sensing analysis is object detection: locating objects of interest on the Earth's surface in overhead imagery. Automated object detection algorithms have advanced by leaps and bounds over the last decade, but they still require vast amounts of labeled data for training, which is expensive and tedious to produce. Any technique that can reduce the resources needed to label objects in overhead imagery is therefore desirable. Most existing datasets for training overhead object detectors are labeled with horizontal bounding boxes [1][2][3][4][5], object-aligned bounding boxes [6][7][8][9][10], or segmentation masks [11][12].

Monitoring of industrial processes is a critical capability in industry and in government to ensure reliability of production cycles, quick emergency response, and national security. Process monitoring allows users to gauge the involvement of an organization in an industrial process or predict the degradation or aging of machine parts in processes taking place at a remote location. Similar to many data science applications, we usually only have access to limited raw data, such as satellite imagery, short video clips, some event logs, and signatures captured by a small set of sensors. To combat data scarcity, we leverage the knowledge of subject matter experts (SMEs) who are familiar with the process. Various process mining techniques have been developed for this type of analysis; typically such approaches combine theoretical process models built based on domain expert insights with ad-hoc integration of available pieces of raw data. Here, we introduce a novel mathematically sound method that integrates theoretical process models (as proposed by SMEs) with interrelated minimal Hidden Markov Models (HMM), built via non-negative tensor factorization and discrete model simulations. Our method consolidates: (a) Theoretical process models development, (b) Discrete model simulations (c) HMM, (d) Joint Non-negative Matrix Factorization (NMF) and Non-negative Tensor Factorization (NTF), and (e) Custom model selection. To demonstrate our methodology and its abilities, we apply it on simple synthetic and real world process models.

Recently, change detection (CD) of remote sensing images have achieved great progress with the advances of deep learning. However, current methods generally deliver incomplete CD regions and irregular CD boundaries due to the limited representation ability of the extracted visual features. To relieve these issues, in this work we propose a novel learning framework named Fully Transformer Network (FTN) for remote sensing image CD, which improves the feature extraction from a global view and combines multi-level visual features in a pyramid manner. More specifically, the proposed framework first utilizes the advantages of Transformers in long-range dependency modeling. It can help to learn more discriminative global-level features and obtain complete CD regions. Then, we introduce a pyramid structure to aggregate multi-level visual features from Transformers for feature enhancement. The pyramid structure grafted with a Progressive Attention Module (PAM) can improve the feature representation ability with additional interdependencies through channel attentions. Finally, to better train the framework, we utilize the deeply-supervised learning with multiple boundaryaware loss functions. Extensive experiments demonstrate that our proposed method achieves a new state-of-the-art performance on four public CD benchmarks. For model reproduction, the source code is released at https://github.com/AI-Zhpp/FTN.

When it comes to electrical power distribution over transmission or distribution wires, a lack of vegetation management around the powerlines can lead to high maintenance costs. Vegetation management is crucial, as uncontrolled vegetation encroachments can lead to forest fires, power outages during a storm, and so on. Identifying these maintenance spots that will need trimming or vegetation management takes time. Often, you will need to conduct a manual survey, or one partially assisted by remote sensing data. Companies must spend time, money, and resources on vegetation encroachment management.

In recent years, the geospatial industry has been developing at a steady pace. This growth implies the addition of satellite constellations that produce a copious supply of satellite imagery and other Remote Sensing data on a daily basis. Sometimes, this information, even if in some cases we are referring to publicly available data, it sits unaccounted for due to the sheer size of it. Processing such large amounts of data with the help of human labour or by using traditional automation methods is not always a viable solution from the standpoint of both time and other resources. Within the present work, we propose an approach for creating a multi-modal and spatio-temporal dataset comprised of publicly available Remote Sensing data and testing for feasibility using state of the art Machine Learning (ML) techniques. Precisely, the usage of Convolutional Neural Networks (CNN) models that are capable of separating different classes of vegetation that are present in the proposed dataset. Popularity and success of similar methods in the context of Geographical Information Systems (GIS) and Computer Vision (CV) more generally indicate that methods alike should be taken in consideration and further analysed and developed.

Oil spill detection has attracted increasing attention in recent years since marine oil spill accidents severely affect environments, natural resources, and the lives of coastal inhabitants. Hyperspectral remote sensing images provide rich spectral information which is beneficial for the monitoring of oil spills in complex ocean scenarios. However, most of the existing approaches are based on supervised and semi-supervised frameworks to detect oil spills from hyperspectral images (HSIs), which require a huge amount of effort to annotate a certain number of high-quality training sets. In this study, we make the first attempt to develop an unsupervised oil spill detection method based on isolation forest for HSIs. First, considering that the noise level varies among different bands, a noise variance estimation method is exploited to evaluate the noise level of different bands, and the bands corrupted by severe noise are removed. Second, kernel principal component analysis (KPCA) is employed to reduce the high dimensionality of the HSIs. Then, the probability of each pixel belonging to one of the classes of seawater and oil spills is estimated with the isolation forest, and a set of pseudo-labeled training samples is automatically produced using the clustering algorithm on the detected probability. Finally, an initial detection map can be obtained by performing the support vector machine (SVM) on the dimension-reduced data, and then, the initial detection result is further optimized with the extended random walker (ERW) model so as to improve the detection accuracy of oil spills. Experiments on airborne hyperspectral oil spill data (HOSD) created by ourselves demonstrate that the proposed method obtains superior detection performance with respect to other state-of-the-art detection approaches.