Good Weather Conditions Research Articles

The PolarLITIS Dataset: Road Scenes Under Fog

Road scene analysis is a fundamental task for both autonomous vehicles and ADAS systems. Nowadays, one can find autonomous vehicles that are able to properly detect objects in the scene in good weather conditions; however, some improvements still need to be done when the visibility is altered. People claim that using some non-conventional sensors such as, infra-red or Lidar, combined with classical vision, enhances road scene analysis in optimal weather conditions. In this work, we present the improvements achieved using polarimetric imaging in the complex situation of some adverse weather conditions. This rich modality is known for its ability to describe an object not only by its intensity information, even under poor illumination or strong reflection. The experimental results have shown that, using a new multimodal dataset, polarimetric imaging was able to provide generic features for both good weather conditions and adverse weather conditions, especially fog. By combining polarimetric images with an adapted learning model, the different detection tasks under fog were improved by about 15% to 44%.

Efficient lane marking detection using deep learning technique with differential and cross-entropy loss

<p>Nowadays, researchers are incorporating many modern and significant features on advanced driver assistance systems (ADAS). Lane marking detection is one of them, which allows the vehicle to maintain the perspective road lane. Conventionally, it is detected through handcrafted and very specialized features and goes through substantial post-processing, which leads to high computation, and less accuracy. Additionally, this conventional method is vulnerable to environmental conditions, making it an unreliable model. Consequently, this research work presents a deep learning-based model that is suitable for diverse environmental conditions, including multiple lanes, different daytime, different traffic conditions, good and medium weather conditions, and so forth. This approach has been derived from plain encode-decode E-Net architecture and has been trained by using the differential and cross-entropy losses for the backpropagation. The model has been trained and tested using 3,600 training and 2,700 testing images from TuSimple, a robust public dataset. Input images from very diverse environmental conditions have ensured better generalization of the model. This framework has reached a max accuracy of 96.61%, with an F1 score of 96.34%, a precision value of 98.91%, and a recall of 93.89%. Besides, this model has shown very small false positive and false negative values of 3.125% and 1.259%, which bits the performance of most of the existing state of art models.</p>

LLDNet: A Lightweight Lane Detection Approach for Autonomous Cars Using Deep Learning.

Lane detection plays a vital role in making the idea of the autonomous car a reality. Traditional lane detection methods need extensive hand-crafted features and post-processing techniques, which make the models specific feature-oriented, and susceptible to instability for the variations on road scenes. In recent years, Deep Learning (DL) models, especially Convolutional Neural Network (CNN) models have been proposed and utilized to perform pixel-level lane segmentation. However, most of the methods focus on achieving high accuracy while considering structured roads and good weather conditions and do not put emphasis on testing their models on defected roads, especially ones with blurry lane lines, no lane lines, and cracked pavements, which are predominant in the real world. Moreover, many of these CNN-based models have complex structures and require high-end systems to operate, which makes them quite unsuitable for being implemented in embedded devices. Considering these shortcomings, in this paper, we have introduced a novel CNN model named LLDNet based on an encoder–decoder architecture that is lightweight and has been tested in adverse weather as well as road conditions. A channel attention and spatial attention module are integrated into the designed architecture to refine the feature maps for achieving outstanding results with a lower number of parameters. We have used a hybrid dataset to train our model, which was created by combining two separate datasets, and have compared the model with a few state-of-the-art encoder–decoder architectures. Numerical results on the utilized dataset show that our model surpasses the compared methods in terms of dice coefficient, IoU, and the size of the models. Moreover, we carried out extensive experiments on the videos of different roads in Bangladesh. The visualization results exhibit that our model can detect the lanes accurately in both structured and defected roads and adverse weather conditions. Experimental results elicit that our designed method is capable of detecting lanes accurately and is ready for practical implementation.

Involvement of men and women in gleaning macro-invertebrates in Baganga, Davao Oriental, Philippines

Gleaning macro-invertebrates is an essential activity for coastal dwellers, providing food and a source of income. Despite its significance, little is known about its gendered nature in the country. This study aimed to document the gender patterns of men and women involved in gleaning in Davao Oriental. The researcher conducted interviews, field observations, and mapping in two coastal barangays of Baganga, Davao Oriental. Men and women engaged in gathering macro-invertebrates were noticeably different regarding the methods used, presence and absence of target species, and gleaning grounds. Women were found to be more involved in gleaning than men. The accessibility of women to macro-invertebrate resources was influenced by factors such as distance to gleaning grounds, occurrences of low tide, and good weather conditions. However, accessibility to economically important species limits women due to their lack of implements and fear of waves and even depths. The tools in gleaning used by both men and women in gathering macro-invertebrates were exploitative, which may pose potential destruction to seagrass beds, if unregulated. Men can quickly shift gleaning grounds to look for high-value species, whereas women generally stay at shallow intertidal areas to gather preferably low-valued species. High dependence on gleaning was observed providing income for both men and women in Kinablangan and food source for men and women in San Victor. Though women had higher gleaning efforts than men, gleaning provides them low daily revenues.

On Secrecy Performance of SWIPT Energy-Harvesting Relay Jamming Based Mixed RF-FSO Systems

This paper proposes a simultaneous wireless information and power transfer (SWIPT) energy-harvesting relay jamming based mixed RF/FSO system, and studies its security performance optimization in the presence of an eavesdropper. In this work, the RF and FSO channels experience Nakagami-m fading distribution and Málaga(M) turbulence, respectively. A two-hop decode-and-forward (DF) relay is presented in the system, and the effect of pointing errors is considered. The presence of a nearby single antenna eavesdropper that attempts to eavesdrop on the transmission is also modeled. In order to prevent eavesdropping, the relay introduces the SWIPT structure to control information delivery and wireless energy recharging. The closed expressions of secrecy outage probability (SOP) and average secrecy capacity (ASC) of the mixed RF/FSO system are derived for the above system model. In addition, the closed-form expression of the asymptotic results for SOP and ASC are derived when signal-to-noise ratios at relay and legitimate destinations tend to infinity. The correctness of these expressions is verified using the Monte Carlo method. The influence of various key factors on the safety performance of the system is analyzed by simulations. The results show that the safety performance of the system is considerably improved under good weather conditions as well as by increasing the signal-interference noise ratio, number of interferer antennas, power distribution factor and energy conversion efficiency. This study provides a new system structure and a good theoretical basis for evaluating the physical layer security performance of the mixed RF/FSO system.

Comparison between Automated and Manual Detection of Lava Fountains from Fixed Monitoring Thermal Cameras at Etna Volcano, Italy

The Etna volcano is renowned worldwide for its extraordinary lava fountains that rise several kilometers above the vent and feed eruptive columns, then drift hundreds of kilometers away from the source. The Italian Istituto Nazionale di Geofisica e Vulcanologia-Osservatorio Etneo (INGV-OE) is responsible for the monitoring of Mt. Etna, and for this reason, has deployed a network of visible and thermal cameras around the volcano. From these cameras, INGV-OE keeps a keen eye, and is able to observe the eruptive activity, promptly advising the civil protection and aviation authorities of any changes, as well as quantifying the spread of lava flows and the extent of pyroclastic and ash plumes by using a careful analysis of the videos recorded by the monitoring cameras. However, most of the work involves analysis carried out by hand, which is necessarily approximate and time-consuming, thus limiting the usefulness of these results for a prompt hazard assessment. In addition, the start of lava fountains is often a gradual process, increasing in strength from Strombolian activity, to intermediate explosive activity, and eventually leading to sustained lava fountains. The thresholds between these different fields (Strombolian, Intermediate, and lava fountains) are not clear cut, and are often very difficult to distinguish by a manual analysis of the images. In this paper, we presented an automated routine that, when applied to thermal images and with good weather conditions, allowed us to detect (1) the starting and ending time of each lava fountain, (2) the area occupied by hot pyroclasts, (3) the elevation reached by the lava fountains over time, and (4) eventually, to calculate in real-time the erupted volume of pyroclasts, giving results close to the manual analysis but more focused on the sustained portion of the lava fountain, which is also the most dangerous. This routine can also be applied to other active volcanoes, allowing a prompt and uniform definition of the timing of the lava fountain eruptive activity, as well as the magnitude and intensity of the event.

Microbial activity and community structure in PM2 .5 at different heights in ground boundary layer of Beijing atmosphere under various air quality levels.

The outbreak of the COVID-19 epidemic is a reminder that aerosols have important health effects as a potential route for disease transmission. Biological components in aerosols (especially PM2.5 ) may pose potential threats to humans as pathogens and allergens. Research on PM2.5 and biological components currently focuses mainly on polluted conditions, with less emphasis on clean environments. Sampling has also been primarily based on a single point with a lack of data at different positions. In this study, a modified fluorescein diacetate hydrolysis method was used to measure microbial activity in PM2.5 at different altitudes over a year in Beijing, China. A high-throughput sequencing method was used to study the microbial community. Results showed that microbial activity 1.5m (0.0465 ng m-3 ) above the ground was higher than 31.5m (0.0348 ng m-3 ). There was higher microbial activity at both heights during spring. Furthermore, a positive correlation was observed between microbial activity and relative abundance of dominant species. Microbial activity increased during autumn and winter increased alongside the pollution level, but in spring higher levels of microbial activity were observed in excellent or good weather conditions. The results from this study are valuable for further research regarding the biological components of atmospheric PM, the prevention of biological pollution, and establishing a comprehensive air quality evaluation system.

Reconstruction and Synthesis of Lidar Point Clouds of Spray

Lidars are commonly used on autonomous vehicles, but their performance can be significantly affected by adverse weather. A number of studies have been devoted to analyzing and improving lidars’ performance in rain, fog, and snow. Yet, relatively little attention has been paid to road spray which occurs when vehicles travel on wet surfaces at high speed. Road spray produced by the vehicles causes false positive point measurements in lidar scans. To evaluate the performance of lidar perception systems or train learning-based perception models, a large amount of data collected in various weather conditions are needed. Unfortunately, collecting spray data is challenging due to the requirements for certain weather conditions (e.g., heavy rain), and vehicles with high speed. In this letter, we propose the first data-driven method combined with simulation to reconstruct and synthesize spray data. The proposed pipeline can be applied to data augmentation by adding spray effects to existing lidar data collected under good weather conditions. We compare the performance of vehicle detection models trained with and without augmented data. The model trained with augmented data achieve significant performance improvement given real-world spray-affected point cloud data.

Coupled steroid and phosphorus leaching from cattle slurry at lysimeter scale

Water quality degradation can be caused by excessive agricultural nutrient transfers from fertilised soils exposed to wet weather. Mitigation measures within the EU Nitrates Directive aim to reduce this pressure by including ‘closed’ fertiliser spreading periods during wet months. For organic fertilisers such as slurry and manure, this closed period requires sufficient on-farm winter storage and good weather conditions to relieve storage at the end of the period. Therefore, robust scientific evidence is needed to support the measure. Incidental nutrient transfers of recently applied organic fertilisers in wet weather can also be complicated by synchronous transfers from residual soil stores and tracing is required for risk assessments. The combination of nutrient monitoring and biomarker analyses may aid this and one such biomarker suite is faecal steroids. Accordingly, this study investigated the persistence of steroids and their association with phosphorus during leaching episodes. The focus was on the coupled behaviour of steroids and total phosphorus (TP) concentrations in sub-surface hydrological pathways. Cattle slurry was applied to monolith lysimeters either side of a closed period and concentrations of both steroids and TP were monitored in the leachate. The study showed no significant effect of the treatment (average p = 0.17), though tracer concentrations did significantly change over time (average p = 0.001). While the steroidal concentration ratio was validated for herbivorous faecal pollution in the leachate, there was a weak positive correlation between the steroids and TP. Further investigation at more natural scales (hillslope/catchment) is required to confirm tracer behaviours/correlations and to compliment this sub-surface pathway study.

An efficient encode-decode deep learning network for lane markings instant segmentation

<span lang="EN-US">Nowadays, advanced driver assistance systems (ADAS) has been incorporated with a distinct type of progressive and essential features. One of the most preliminary and significant features of the ADAS is lane marking detection, which permits the vehicle to keep in a particular road lane itself. It has been detected by utilizing high-specialized, handcrafted features and distinct post-processing approaches lead to less accurate, less efficient, and high computational framework under different environmental conditions. Hence, this research proposed a simple encode-decode deep learning approach under distinguishing environmental effects like different daytime, multiple lanes, different traffic condition, good and medium weather conditions for detecting the lane markings more accurately and efficiently. The proposed model is emphasized on the simple encode-decode Seg-Net framework incorporated with VGG16 architecture that has been trained by using the inequity and cross-entropy losses to obtain more accurate instant segmentation result of lane markings. The framework has been trained and tested on a vast public dataset named Tusimple, which includes around 3.6K training and 2.7 k testing image frames of different environmental conditions. The model has noted the highest accuracy, 96.61%, F1 score 96.34%, precision 98.91%, and recall 93.89%. Also, it has <span>also obtained the lowest 3.125% false positive and 1.259% false-negative value, which transcended some of the previous researches. It is expected to</span> assist significantly in the field of lane markings detection applying deep neural networks.</span>

3D Object Detection with SLS-Fusion Network in Foggy Weather Conditions.

The role of sensors such as cameras or LiDAR (Light Detection and Ranging) is crucial for the environmental awareness of self-driving cars. However, the data collected from these sensors are subject to distortions in extreme weather conditions such as fog, rain, and snow. This issue could lead to many safety problems while operating a self-driving vehicle. The purpose of this study is to analyze the effects of fog on the detection of objects in driving scenes and then to propose methods for improvement. Collecting and processing data in adverse weather conditions is often more difficult than data in good weather conditions. Hence, a synthetic dataset that can simulate bad weather conditions is a good choice to validate a method, as it is simpler and more economical, before working with a real dataset. In this paper, we apply fog synthesis on the public KITTI dataset to generate the Multifog KITTI dataset for both images and point clouds. In terms of processing tasks, we test our previous 3D object detector based on LiDAR and camera, named the Spare LiDAR Stereo Fusion Network (SLS-Fusion), to see how it is affected by foggy weather conditions. We propose to train using both the original dataset and the augmented dataset to improve performance in foggy weather conditions while keeping good performance under normal conditions. We conducted experiments on the KITTI and the proposed Multifog KITTI datasets which show that, before any improvement, performance is reduced by 42.67% in 3D object detection for Moderate objects in foggy weather conditions. By using a specific strategy of training, the results significantly improved by 26.72% and keep performing quite well on the original dataset with a drop only of 8.23%. In summary, fog often causes the failure of 3D detection on driving scenes. By additional training with the augmented dataset, we significantly improve the performance of the proposed 3D object detection algorithm for self-driving cars in foggy weather conditions.

Influence of weather conditions on the traffic flow parameters

Changes in weather conditions affect people's lifestyle in many ways. This paper presents the influence of weather conditions on the parameters of traffic flows on a two - lane suburban road. Main goal for comparing parameters of traffic flow for bad and good weather conditions is to draw conclusions about which parameters and to what extent are affected by different weather conditions. Based on the foreign literature, it is necessary to define an appropriate research methodology adapted to local conditions and apply it in an adequate way in order to obtain the most accurate results and draw conclusions. In this study, the influence of rainfall on the reduction of anxiety and traffic flow in the vicinity of the town of Loznica is presented. Based on meteorological data for 2019, a sample of days and hours when it rained was determined and compared with days when there was no precipitation.

Large Two-Stroke Marine Diesel Engine Operation with a High-Pressure SCR System in Heavy Weather Conditions

Abstract The transient performance of a direct-drive large two-stroke marine diesel engine, installed in a vessel operating in a seaway with heavy weather, is investigated via simulation. The main engine of the ship is equipped with a selective catalytic reduction (SCR) aftertreatment system for compliance with the latest International Maritime Organization (IMO) rules for NOx reduction, IMO Tier III. Because of limitations of exhaust gas temperature at the inlet of SCR systems and the low temperature exhaust gases produced by marine diesel engines, in marine applications, the SCR system is installed on the high-pressure side of the turbine. When a ship sails in heavy weather, it experiences a resistance increase, wave-induced motions, and a time-varying flow field in the propeller, induced by ship motions. This results in a fluctuation of the propeller torque demand and, thus, a fluctuation in engine power and exhaust gas temperature, which can affect engine and SCR performance. To investigate this phenomenon and take into account the engine–propeller interaction, the entire propulsion plant was modeled, namely, the slow-speed diesel propulsion engine, the high-pressure SCR system, the directly driven propeller, and the ship’s hull. To simulate the transient propeller torque demand, a propeller model was used, and torque variations due to ship motions were taken into account. Ship motions in waves and wave-added resistance were calculated for regular and irregular waves using a 3D panel code. The coupled model was validated against available measured data from a shipboard propulsion system in good weather conditions. The model was then used to simulate the behavior of a Tier III marine propulsion plant during acceleration from low to medium load, in the presence of regular and irregular waves. The effect of the time-varying propeller demand on the engine and the SCR system was investigated. Introduction The effect of waves on a marine propulsion system is a complex phenomenon involving interactions between different subsystems of the propulsion plant, i.e., the prime mover, the propeller, and the ship’s hull. Ships sailing in heavy weather conditions experience a resistance increase, wave-induced motions, and a time-varying flow field in the propeller. This leads to a fluctuation of the propeller torque demand which results in a fluctuation in engine-produced power and exhaust gas temperature.

French Motorcyclists and the 80 km/hr Speed Limit: Conditionality and Risk-Taking with Regard to Age, Type and Power of Motorbike

After months of controversy, the speed limit on two-way secondary French roads without a median strip was reduced from 90 to 80 km/hr on 1 July 2018. Thousands of car drivers and motorcyclists have since protested against this measure, and the present study concerned the latter group. Starting from the Theory of Conditionality in the field of social thinking, which holds that prescriptions are massively conditional, a specific questionnaire based on 23 conditional scripts has been devised. A total of 661 male motorcyclists were asked about the conditions justifying the transgression of the 80 km/hr limit. Results showed that those motorcyclists who exceeded this limit did so by at least by 20 km/hr. A personalized assessment of speed, the pleasure associated with driving, and good road and weather conditions were the most conditional situations, while situations linked to the risk of loss of control were the least conditional. This study suggests that the determinants of risk-taking must be studied at the group and context levels, and that behavior is influenced by the system of social norms. Although this system may conflict with the Highway Code, it reflects an adaptation of the group linked to a need for meaning. Received: 7 July 2021 / Accepted: 12 August 2021 / Published: 5 September 2021

Adaptive Multi-modal Fusion Instance Segmentation for CAEVs in Complex Conditions: Dataset, Framework and Verifications

Current works of environmental perception for connected autonomous electrified vehicles (CAEVs) mainly focus on the object detection task in good weather and illumination conditions, they often perform poorly in adverse scenarios and have a vague scene parsing ability. This paper aims to develop an end-to-end sharpening mixture of experts (SMoE) fusion framework to improve the robustness and accuracy of the perception systems for CAEVs in complex illumination and weather conditions. Three original contributions make our work distinctive from the existing relevant literature. The Complex KITTI dataset is introduced which consists of 7481 pairs of modified KITTI RGB images and the generated LiDAR dense depth maps, and this dataset is fine annotated in instance-level with the proposed semi-automatic annotation method. The SMoE fusion approach is devised to adaptively learn the robust kernels from complementary modalities. Comprehensive comparative experiments are implemented, and the results show that the proposed SMoE framework yield significant improvements over the other fusion techniques in adverse environmental conditions. This research proposes a SMoE fusion framework to improve the scene parsing ability of the perception systems for CAEVs in adverse conditions.

Restoring Raindrops Using Attentive Generative Adversarial Networks

Artificial intelligence technologies and vision systems are used in various devices, such as automotive navigation systems, object-tracking systems, and intelligent closed-circuit televisions. In particular, outdoor vision systems have been applied across numerous fields of analysis. Despite their widespread use, current systems work well under good weather conditions. They cannot account for inclement conditions, such as rain, fog, mist, and snow. Images captured under inclement conditions degrade the performance of vision systems. Vision systems need to detect, recognize, and remove noise because of rain, snow, and mist to boost the performance of the algorithms employed in image processing. Several studies have targeted the removal of noise resulting from inclement conditions. We focused on eliminating the effects of raindrops on images captured with outdoor vision systems in which the camera was exposed to rain. An attentive generative adversarial network (ATTGAN) was used to remove raindrops from the images. This network was composed of two parts: an attentive-recurrent network and a contextual autoencoder. The ATTGAN generated an attention map to detect rain droplets. A de-rained image was generated by increasing the number of attentive-recurrent network layers. We increased the number of visual attentive-recurrent network layers in order to prevent gradient sparsity so that the entire generation was more stable against the network without preventing the network from converging. The experimental results confirmed that the extended ATTGAN could effectively remove various types of raindrops from images.

A NOVEL ACCELEROMETER-BASED METHOD FOR ESTIMATING THE TRANSVERSE DISPLACEMENT OF LIGHT PIER RAILROAD BRIDGES AND SHAKING TABLE TEST VERIFICATION

As of 2019, the nation's railroads have delivered a total of 4.38 billion tons of freight. A significant number of current piers are light piers built earlier, and are often deflected due to their limited lateral stiffness. The deflection of piers is getting increasingly evident as the line speeds up and freight increases. On the other hand, the railroad requires a high deformity of train tracks to avoid derailment. Hence, a method is in need that can monitor the track displacement in harsh environments timely and continuously. Current methods depend on costly machines and accurate models which are available only under good weather conditions. This paper presents a technique to track the lateral movement of light piers with accelerometers. It utilizes low-cost and easy-to-mount accelerometers for persistent and reliable detection of high-dynamic and low-frequency pseudo-static displacements. A novel shaking table is built and the concept is proven by numerical experiments. Compared with the linear follower displacement meter, the peak error is 11.80% and the mean error is 8.05%.

Data Augmentation of Automotive LIDAR Point Clouds under Adverse Weather Situations.

In contrast to previous works on data augmentation using LIDAR (Light Detection and Ranging), which mostly consider point clouds under good weather conditions, this paper uses point clouds which are affected by spray. Spray water can be a cause of phantom braking and understanding how to handle the extra detections caused by it is an important step in the development of ADAS (Advanced Driver Assistance Systems)/AV (Autonomous Vehicles) functions. The extra detections caused by spray cannot be safely removed without considering cases in which real solid objects may be present in the same region in which the detections caused by spray take place. As collecting real examples would be extremely difficult, the use of synthetic data is proposed. Real scenes are reconstructed virtually with an added extra object in the spray region, in a way that the detections caused by this obstacle match the characteristics a real object in the same position would have regarding intensity, echo number and occlusion. The detections generated by the obstacle are then used to augment the real data, obtaining, after occlusion effects are added, a good approximation of the desired training data. This data is used to train a classifier achieving an average F-Score of 92. The performance of the classifier is analyzed in detail based on the characteristics of the synthetic object: size, position, reflection, duration. The proposed method can be easily expanded to different kinds of obstacles and classifier types.

The impact of big-city smog and pollution on a pitch with artificial turf on the body’s reaction during physical efforts (on the example of female football player training)

Introduction. For health-promoting purposes, in terms of optimal human development, physical activity plays an essential role in efficient functioning of the body, but it is important for physical development that this activity takes place in good weather conditions, especially when it comes to clean air. Unfortunately, the problem with smog nowadays occurs almost everywhere, especially in large cities, where poor air conditions are often combined with the development of agglomerations (Duda et al. 2020). The level of smog may also increase in the event of greater air pollution, and such a state can often be seen on sports pitches with artificial turfs (Kmak 2016) Objective. The aim of the study was to identify the human body’s response to physical effort, taking the degree of air pollution related to smog into account. This reaction was also evaluted in conditions of activity on an artificial football pitch, where the concentration of particulate air pollution on the area is greater due to the release of dust from abrasive granules present on the grass of artificial football pitches. Research material and methods. The study was conducted among 20 women recreationally training football. The average age of the participants was 22.5 years. Continuous research in the years 2019-2021 was carried out during programme training in football on an artificial football pitch at a sports facility in Kraków. The body’s reaction (resting and exercise heart rate) was tested with a sportester in conditions of smog and clean air (Samsung AX60R5080WD/EU air pollution metres, Air Visual Pro). Results and conclusions. The test results allowed to state that the contamination level of the pitch has impact on the body’s reaction (higher HR value). It has also been observed that artificial pitches, due to the abrasion of granules, are characterised by greater particulate air pollution.

Assessment of Ecosystem Services and Disservices in Urban Environment Using Multispectral Image Analysis and Geospatial Mapping

Ecosystem service and disservice components have a great impact on the environment as well as on urban life. The aim of the research is to assess the impact of ecosystem services and disservices by tracking the change over a span of 15 years (2005-2020) using satellite image analysis. It was conducted in wards 23 and 29 of Khulna City Corporation (KCC), an area adjacent to the river. The overall work breakdown has been rounded up in a methodological framework which cleaved into two parts focusing on ecosystem services, disservices and multispectral image analysis steps. Multispectral image analysis has been done using Normalized Difference Vegetation Index (NDVI) and Normalized Difference Build-up Index (NDBI). On the other hand, geospatial analysis has been done using Digital Elevation Model (DEM), Service Area Analysis (SAV) and other mapping tool including Geographic Information System (GIS) to show the changed impact of both ecosystem components. The study areas attain provisioning, habitat, cultural and regulatory service components and the disservice elements have been sorted out according to sources. Comparative study showed increase in the buildup area; vegetation coverage because of increasing vegetation, cultivation and gardening practices; good weather and water circulation condition and water body conservation which has significant positive impact on the area. The disservice component like poor drainage condition and solid waste management system and risky infrastructures have negative impact, need to address for the betterment of residence and environment.